R-universe search: topic:time-series

joshuaulrich

xts:eXtensible Time Series

Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.

Maintained by Joshua M. Ulrich. Last updated 4 months ago.

c time-series

221 stars 18.38 score 12k scripts 654 dependents

sebkrantz

collapse:Advanced and Fast Data Transformation

A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It is well integrated with base R classes, 'dplyr'/'tibble', 'data.table', 'sf', 'units', 'plm' (panel-series and data frames), and 'xts'/'zoo'.

Maintained by Sebastian Krantz. Last updated 7 days ago.

data-aggregation data-analysis data-manipulation data-processing data-science data-transformation econometrics high-performance panel-data scientific-computing statistics time-series weighted weights cpp openmp

672 stars 16.68 score 708 scripts 99 dependents

joshuaulrich

quantmod:Quantitative Financial Modelling Framework

Specify, build, trade, and analyse quantitative financial trading strategies.

Maintained by Joshua M. Ulrich. Last updated 12 hours ago.

algorithmic-trading charting data-import finance time-series

840 stars 16.18 score 8.1k scripts 343 dependents

business-science

timetk:A Tool Kit for Working with Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

Maintained by Matt Dancho. Last updated 1 years ago.

coercion coercion-functions data-mining dplyr forecast forecasting forecasting-models machine-learning series-decomposition series-signature tibble tidy tidyquant tidyverse time time-series timeseries

626 stars 14.20 score 4.0k scripts 16 dependents

config-i1

smooth:Forecasting Using State Space Models

Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>), Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>), SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>), Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>), Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>) and several simulation functions. It also allows dealing with intermittent demand based on the iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).

Maintained by Ivan Svetunkov. Last updated 12 days ago.

arima arima-forecasting ces ets exponential-smoothing forecast state-space time-series openblas cpp

90 stars 13.83 score 412 scripts 25 dependents

business-science

tidyquant:Tidy Quantitative Financial Analysis

Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples.

Maintained by Matt Dancho. Last updated 1 months ago.

dplyr financial-analysis financial-data financial-statements multiple-stocks performance-analysis performanceanalytics quantmod stock stock-exchanges stock-indexes stock-lists stock-performance stock-prices stock-symbol tidyverse time-series timeseries xts

872 stars 13.34 score 5.2k scripts

asardaes

dtwclust:Time Series Clustering Along with Optimizations for the Dynamic Time Warping Distance

Time series clustering along with optimized techniques related to the Dynamic Time Warping distance and its corresponding lower bounds. Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole clustering are available. Functionality can be easily extended with custom distance measures and centroid definitions. Implementations of DTW barycenter averaging, a distance based on global alignment kernels, and the soft-DTW distance and centroid routines are also provided. All included distance functions have custom loops optimized for the calculation of cross-distance matrices, including parallelization support. Several cluster validity indices are included.

Maintained by Alexis Sarda. Last updated 8 months ago.

clustering dtw time-series openblas cpp

262 stars 12.35 score 406 scripts 14 dependents

steffenmoritz

imputeTS:Time Series Missing Value Imputation

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: 'Mean', 'LOCF', 'Interpolation', 'Moving Average', 'Seasonal Decomposition', 'Kalman Smoothing on Structural Time Series models', 'Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.

Maintained by Steffen Moritz. Last updated 3 years ago.

data-visualization imputation imputation-algorithm imputets missing-data time-series cpp

162 stars 12.18 score 1.9k scripts 27 dependents

christophsax

seasonal:R Interface to X-13-ARIMA-SEATS

Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment software by the US Census Bureau. It offers full access to almost all options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model search, outlier detection and support for user defined holiday variables, such as Chinese New Year or Indian Diwali. A graphical user interface can be used through the 'seasonalview' package. Uses the X-13-binaries from the 'x13binary' package.

Maintained by Christoph Sax. Last updated 29 days ago.

seasonal-adjustment time-series

120 stars 12.03 score 1.1k scripts 8 dependents

kingaa

pomp:Statistical Inference for Partially Observed Markov Processes

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Maintained by Aaron A. King. Last updated 9 days ago.

abc b-spline differential-equations dynamical-systems iterated-filtering likelihood likelihood-free markov-chain-monte-carlo markov-model mathematical-modelling measurement-error particle-filter sequential-monte-carlo simulation-based-inference sobol-sequence state-space statistical-inference stochastic-processes time-series openblas

114 stars 11.74 score 1.3k scripts 4 dependents

robjhyndman

tsfeatures:Time Series Feature Extraction

Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.

Maintained by Rob Hyndman. Last updated 8 months ago.

feature-extraction time-series

257 stars 11.55 score 268 scripts 22 dependents

helske

KFAS:Kalman Filter and Smoother for Exponential Family State Space Models

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

Maintained by Jouni Helske. Last updated 7 months ago.

dynamic-linear-model exponential-family fortran gaussian-models state-space time-series openblas

64 stars 10.97 score 242 scripts 16 dependents

ovvo-financial

NNS:Nonlinear Nonparametric Statistics

Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Maintained by Fred Viole. Last updated 21 hours ago.

clustering econometrics machine-learning nonlinear nonparametric partial-moments statistics time-series cpp

72 stars 10.77 score 66 scripts 3 dependents

ropensci

tsbox:Class-Agnostic Time Series

Time series toolkit with identical behavior for all time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo', 'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.

Maintained by Christoph Sax. Last updated 5 months ago.

graphics time-series

150 stars 10.61 score 496 scripts 4 dependents

business-science

modeltime:The Tidymodels Extension for Time Series Modeling

The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

Maintained by Matt Dancho. Last updated 5 months ago.

arima data-science deep-learning ets forecasting machine-learning machine-learning-algorithms modeltime prophet tbats tidymodeling tidymodels time time-series time-series-analysis timeseries timeseries-forecasting

551 stars 10.61 score 1.1k scripts 7 dependents

business-science

tibbletime:Time Aware Tibbles

Built on top of the 'tibble' package, 'tibbletime' is an extension that allows for the creation of time aware tibbles. Some immediate advantages of this include: the ability to perform time-based subsetting on tibbles, quickly summarising and aggregating results by time periods, and creating columns that can be used as 'dplyr' time-based groups.

Maintained by Davis Vaughan. Last updated 4 months ago.

periodicity tibble time time-series timeseries cpp

177 stars 10.51 score 644 scripts 2 dependents

sciviews

pastecs:Package for Analysis of Space-Time Ecological Series

Regularisation, decomposition and analysis of space-time series. The pastecs R package is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 functionalities to R.

Maintained by Philippe Grosjean. Last updated 1 years ago.

ecology time-series

4 stars 10.34 score 2.1k scripts 13 dependents

atsa-es

MARSS:Multivariate Autoregressive State-Space Modeling

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.

Maintained by Elizabeth Eli Holmes. Last updated 1 years ago.

multivariate-timeseries state-space-models statistics time-series

52 stars 10.34 score 596 scripts 3 dependents

cdriveraus

ctsem:Continuous Time Structural Equation Modelling

Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . Stan based functions are not available on 32 bit Windows systems at present. <https://cdriver.netlify.app/> contains some tutorial blog posts.

Maintained by Charles Driver. Last updated 24 days ago.

stochastic-differential-equations time-series cpp

42 stars 9.58 score 366 scripts 1 dependents

business-science

anomalize:Tidy Anomaly Detection

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.

Maintained by Matt Dancho. Last updated 1 years ago.

anomaly anomaly-detection decomposition detect-anomalies iqr time-series

339 stars 9.56 score 332 scripts

microsoft

finnts:Microsoft Finance Time Series Forecasting Framework

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

Maintained by Mike Tokic. Last updated 1 months ago.

business data-science feature-selection finance finnts forecasting machine-learning microsoft time-series

194 stars 9.30 score 39 scripts

business-science

sweep:Tidy Tools for Forecasting

Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'.

Maintained by Matt Dancho. Last updated 1 years ago.

broom forecast forecasting-models prediction tidy tidyverse time time-series timeseries

155 stars 9.23 score 399 scripts 1 dependents

ramikrispin

TSstudio:Functions for Time Series Analysis and Forecasting

Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.

Maintained by Rami Krispin. Last updated 2 years ago.

forecasting time-series timeseries tsstudio visualization

425 stars 9.02 score 656 scripts

constantino-garcia

nonlinearTseries:Nonlinear Time Series Analysis

Functions for nonlinear time series analysis. This package permits the computation of the most-used nonlinear statistics/algorithms including generalized correlation dimension, information dimension, largest Lyapunov exponent, sample entropy and Recurrence Quantification Analysis (RQA), among others. Basic routines for surrogate data testing are also included. Part of this work was based on the book "Nonlinear time series analysis" by Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).

Maintained by Constantino A. Garcia. Last updated 6 months ago.

chaos chaotic-systems nonlinear-dynamics nonlinear-time-series time-series openblas cpp

36 stars 9.01 score 123 scripts 7 dependents

fastverse

fastverse:A Suite of High-Performance Packages for Statistics and Data Manipulation

Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 'kit' and 'magrittr', that jointly only depend on 'Rcpp'. The 'fastverse' can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.

Maintained by Sebastian Krantz. Last updated 1 months ago.

c cpp data-aggregation data-manipulation data-science data-transformation high-performance low-dependency panel-data statistical-computing time-series weights

266 stars 8.98 score 222 scripts

mattcowgill

readabs:Download and Tidy Time Series Data from the Australian Bureau of Statistics

Downloads, imports, and tidies time series data from the Australian Bureau of Statistics <https://www.abs.gov.au/>.

Maintained by Matt Cowgill. Last updated 27 days ago.

abs australia australian-bureau-of-statistics australian-data statistics tidy-data time-series

104 stars 8.85 score 180 scripts

helske

seqHMM:Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Designed for fitting hidden (latent) Markov models and mixture hidden Markov models for social sequence data and other categorical time series. Also some more restricted versions of these type of models are available: Markov models, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and hidden Markov models. Models are estimated using maximum likelihood via the EM algorithm and/or direct numerical maximization with analytical gradients. All main algorithms are written in C++ with support for parallel computation. Documentation is available via several vignettes in this page, and the paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).

Maintained by Jouni Helske. Last updated 2 years ago.

categorical-data em-algorithm hidden-markov-models hmm mixture-markov-models time-series openblas cpp openmp

98 stars 8.52 score 92 scripts 1 dependents

chuhousen

amerifluxr:Interface to 'AmeriFlux' Data Services

Programmatic interface to the 'AmeriFlux' database (<https://ameriflux.lbl.gov/>). Provide query, download, and data summary tools.

Maintained by Housen Chu. Last updated 3 months ago.

ameriflux api carbon-flux data time-series

22 stars 8.36 score 29 scripts 15 dependents

business-science

modeltime.ensemble:Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Maintained by Matt Dancho. Last updated 8 months ago.

ensemble ensemble-learning forecast forecasting modeltime stacking stacking-ensemble tidymodels time time-series timeseries

77 stars 8.30 score 143 scripts

mayer79

splitTools:Tools for Data Splitting

Fast, lightweight toolkit for data splitting. Data sets can be partitioned into disjoint groups (e.g. into training, validation, and test) or into (repeated) k-folds for subsequent cross-validation. Besides basic splits, the package supports stratified, grouped as well as blocked splitting. Furthermore, cross-validation folds for time series data can be created. See e.g. Hastie et al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic background on data partitioning and cross-validation.

Maintained by Michael Mayer. Last updated 1 months ago.

cross-validation machine-learning time-series validation

13 stars 8.15 score 169 scripts 4 dependents

ropensci

MODIStsp:Find, Download and Process MODIS Land Products Data

Allows automating the creation of time series of rasters derived from MODIS satellite land products data. It performs several typical preprocessing steps such as download, mosaicking, reprojecting and resizing data acquired on a specified time period. All processing parameters can be set using a user-friendly GUI. Users can select which layers of the original MODIS HDF files they want to process, which additional quality indicators should be extracted from aggregated MODIS quality assurance layers and, in the case of surface reflectance products, which spectral indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster files corresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file are also created. Command-line execution exploiting a previously saved processing options file is also possible, allowing users to automatically update time series related to a MODIS product whenever a new image is available. For additional documentation refer to the following article: Busetto and Ranghetti (2016) <doi:10.1016/j.cageo.2016.08.020>.

Maintained by Luigi Ranghetti. Last updated 8 months ago.

gdal modis modis-data modis-land-products peer-reviewed preprocessing remote-sensing satellite-imagery time-series

156 stars 8.04 score 86 scripts 1 dependents

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

15 stars 7.68 score 59 scripts 4 dependents

nredell

forecastML:Time Series Forecasting with Machine Learning Methods

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Maintained by Nickalus Redell. Last updated 5 years ago.

deep-learning direct-forecasting forecast forecasting machine-learning multi-step-ahead-forecasting neural-network python time-series

130 stars 7.64 score 134 scripts

zhaokg

Rbeast:Bayesian Change-Point Detection and Time Series Decomposition

Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

Maintained by Kaiguang Zhao. Last updated 7 months ago.

anomoly-detection bayesian-time-series breakpoint-detection changepoint-detection interrupted-time-series seasonality-analysis structural-breakpoint technical-analysis time-series time-series-decomposition trend trend-analysis

302 stars 7.63 score 89 scripts

spsanderson

healthyR.ts:The Time Series Modeling Companion to 'healthyR'

Hospital time series data analysis workflow tools, modeling, and automations. This library provides many useful tools to review common administrative time series hospital data. Some of these include average length of stay, and readmission rates. The aim is to provide a simple and consistent verb framework that takes the guesswork out of everything.

Maintained by Steven Sanderson. Last updated 6 months ago.

ai arima-forecasting arima-model ets forecasting ggplot2 machine-learning modeling prophet time-series time-series-analysis workflows

19 stars 7.58 score 56 scripts 1 dependents

hendersontrent

theft:Tools for Handling Extraction of Features from Time Series

Consolidates and calculates different sets of time-series features from multiple 'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, 'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, 'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

Maintained by Trent Henderson. Last updated 2 months ago.

data-visualisation data-visualization dimensionality-reduction machine-learning time-series

40 stars 7.48 score 50 scripts 1 dependents

christophsax

tempdisagg:Methods for Temporal Disaggregation and Interpolation of Time Series

Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series, where either the sum, the mean, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series classes.

Maintained by Christoph Sax. Last updated 2 years ago.

time-series

41 stars 7.43 score 106 scripts 1 dependents

earowang

sugrrants:Supporting Graphs for Analysing Time Series

Provides 'ggplot2' graphics for analysing time series data. It aims to fit into the 'tidyverse' and grammar of graphics framework for handling temporal data.

Maintained by Earo Wang. Last updated 1 years ago.

statistical-graphics time-series

82 stars 7.42 score 214 scripts 1 dependents

matrix-profile-foundation

tsmp:Time Series with Matrix Profile

A toolkit implementing the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.

Maintained by Francisco Bischoff. Last updated 3 years ago.

algorithm matrix-profile motif-search time-series cpp

72 stars 7.29 score 179 scripts 1 dependents

petolau

TSrepr:Time Series Representations

Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

Maintained by Peter Laurinec. Last updated 5 years ago.

data-analysis data-mining data-mining-algorithms data-science representation time-series time-series-analysis time-series-classification time-series-clustering time-series-data-mining time-series-representations cpp

97 stars 7.23 score 117 scripts

robjhyndman

Mcomp:Data from the M-Competitions

The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.

Maintained by Rob Hyndman. Last updated 9 months ago.

forecasting time-series

11 stars 7.00 score 288 scripts 2 dependents

doccstat

fastcpd:Fast Change Point Detection via Sequential Gradient Descent

Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.

Maintained by Xingchi Li. Last updated 11 days ago.

change-point-detection cpp custom-function gradient-descent lasso linear-regression logistic-regression offline pelt penalized-regression poisson-regression quasi-newton statistics time-series warm-start fortran openblas cpp openmp

22 stars 7.00 score 7 scripts

mbalcilar

mFilter:Miscellaneous Time Series Filters

The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.

Maintained by Mehmet Balcilar. Last updated 2 years ago.

baxter-king-filter butterworth-filter christiano-fitzgerald-filter filters hodrick-prescott-filter macroeconomics time-series trigonometric-regression-filter

5 stars 6.99 score 732 scripts 2 dependents

ykang

gratis:Generating Time Series with Diverse and Controllable Characteristics

Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.

Maintained by Feng Li. Last updated 12 months ago.

data-generation mixture-autoregressive statistical-computing time-series

76 stars 6.98 score 25 scripts

kvasilopoulos

exuber:Econometric Analysis of Explosive Time Series

Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>.The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.

Maintained by Kostas Vasilopoulos. Last updated 1 years ago.

dickey-fuller explosive-dynamics simulation time-series openblas cpp

29 stars 6.83 score 77 scripts

hendersontrent

Rcatch22:Calculation of 22 CAnonical Time-Series CHaracteristics

Calculate 22 summary statistics coded in C on time-series vectors to enable pattern detection, classification, and regression applications in the feature space as proposed by Lubba et al. (2019) <doi:10.1007/s10618-019-00647-x>.

Maintained by Trent Henderson. Last updated 6 months ago.

machine-learning time-series cpp

22 stars 6.64 score 22 scripts 2 dependents

business-science

modeltime.resample:Resampling Tools for Time Series Forecasting

A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.

Maintained by Matt Dancho. Last updated 1 years ago.

accuracy-metrics backtesting bootstrap bootstrapping cross-validation forecasting modeltime modeltime-resample resampling statistics tidymodels time-series

19 stars 6.64 score 38 scripts 1 dependents

r-spark

sparklyr.flint:Sparklyr Extension for 'Flint'

This sparklyr extension makes 'Flint' time series library functionalities (<https://github.com/twosigma/flint>) easily accessible through R.

Maintained by Edgar Ruiz. Last updated 3 years ago.

apache-spark data-analysis data-mining data-science distributed distributed-computing flint remote-clusters spark sparklyr statistical-analysis statistics stats summarization summary-statistics time-series time-series-analysis twosigma-flint

9 stars 6.46 score 54 scripts

helske

bssm:Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Maintained by Jouni Helske. Last updated 7 months ago.

bayesian-inference cpp markov-chain-monte-carlo particle-filter state-space time-series openblas cpp openmp

42 stars 6.43 score 11 scripts

helske

walker:Bayesian Generalized Linear Models with Time-Varying Coefficients

Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).

Maintained by Jouni Helske. Last updated 7 months ago.

bayesian generalized-linear-models mcmc stan time-series openblas cpp

44 stars 6.42 score 15 scripts

ygeunkim

bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling

Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Maintained by Young Geun Kim. Last updated 29 days ago.

bayesian bayesian-econometrics bvar eigen forecasting har pybind11 python rcppeigen time-series vector-autoregression cpp openmp

6 stars 6.42 score 25 scripts

jolars

tactile:New and Extended Plots, Methods, and Panel Functions for 'lattice'

Extensions to 'lattice', providing new high-level functions, methods for existing functions, panel functions, and a theme.

Maintained by Johan Larsson. Last updated 2 years ago.

lattice linear-models plotting time-series

7 stars 6.33 score 154 scripts

wilsonfreitas

rbcb:R Interface to Brazilian Central Bank Web Services

The Brazilian Central Bank API delivers many datasets which regard economic activity, regional economy, international economy, public finances, credit indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>. These datasets can be accessed through 'rbcb' functions and can be obtained in different data structures common to R ('tibble', 'data.frame', 'xts', ...).

Maintained by Wilson Freitas. Last updated 1 years ago.

brazilian-data central-bank central-banking cross-currency-rates currency-rates download-currency-rates exchange-rate financial-data time-series

92 stars 6.27 score 184 scripts

fabrice-rossi

mixvlmc:Variable Length Markov Chains with Covariates

Estimates Variable Length Markov Chains (VLMC) models and VLMC with covariates models from discrete sequences. Supports model selection via information criteria and simulation of new sequences from an estimated model. See Bühlmann, P. and Wyner, A. J. (1999) <doi:10.1214/aos/1018031204> for VLMC and Zanin Zambom, A., Kim, S. and Lopes Garcia, N. (2022) <doi:10.1111/jtsa.12615> for VLMC with covariates.

Maintained by Fabrice Rossi. Last updated 11 months ago.

machine-learning markov-chain markov-model statistics time-series cpp

2 stars 6.23 score 20 scripts

danigiro

FoReco:Forecast Reconciliation

Classical (bottom-up and top-down), optimal combination and heuristic point (Di Fonzo and Girolimetto, 2023 <doi:10.1016/j.ijforecast.2021.08.004>) and probabilistic (Girolimetto et al. 2023 <doi:10.1016/j.ijforecast.2023.10.003>) forecast reconciliation procedures for linearly constrained time series (e.g., hierarchical or grouped time series) in cross-sectional, temporal, or cross-temporal frameworks.

Maintained by Daniele Girolimetto. Last updated 3 months ago.

forecasting reconciliation time-series

33 stars 6.19 score 104 scripts

helske

Rlibeemd:Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version from GitHub: <https://github.com/helske/Rlibeemd/>.

Maintained by Jouni Helske. Last updated 2 years ago.

c decomposition eemd emd time-series gsl cpp openmp

39 stars 6.14 score 17 scripts 14 dependents

dylanb95

statespacer:State Space Modelling in 'R'

A tool that makes estimating models in state space form a breeze. See "Time Series Analysis by State Space Methods" by Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.

Maintained by Dylan Beijers. Last updated 2 years ago.

cpp dynamic-linear-model forecasting gaussian-models kalman-filter mathematical-modelling state-space statistical-inference statistical-models structural-analysis time-series openblas cpp openmp

15 stars 6.14 score 37 scripts

ouhscbbmc

Wats:Wrap Around Time Series Graphics

Wrap-around Time Series (WATS) plots for interrupted time series designs with seasonal patterns. Longitudinal trajectories are shown in both Cartesian and polar coordinates. In many scenarios, a WATS plot more clearly shows the existence and effect size of of an intervention. This package accompanies "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs" by Rodgers, Beasley, & Schuelke (2014) <doi:10.1080/00273171.2014.946589>; see 'citation("Wats")' for details.

Maintained by Will Beasley. Last updated 1 months ago.

graphical-analysis oklahoma-city-bombing publication seasonal time-series

7 stars 6.14 score 44 scripts

andrea-luciani

bimets:Time Series and Econometric Modeling

Time series analysis, (dis)aggregation and manipulation, e.g. time series extension, merge, projection, lag, lead, delta, moving and cumulative average and product, selection by index, date and year-period, conversion to daily, monthly, quarterly, (semi)annually. Simultaneous equation models definition, estimation, simulation and forecasting with coefficient restrictions, error autocorrelation, exogenization, add-factors, impact and interim multipliers analysis, conditional equation evaluation, rational expectations, endogenous targeting and model renormalization, structural stability, stochastic simulation and forecast, optimal control.

Maintained by Andrea Luciani. Last updated 4 months ago.

econometrics optimal-control simultaneous-equations stochastic-simulation structural-equation-modeling time-series

15 stars 6.13 score 30 scripts

geobosh

sarima:Simulation and Prediction with Seasonal ARIMA Models

Functions, classes and methods for time series modelling with ARIMA and related models. The aim of the package is to provide consistent interface for the user. For example, a single function autocorrelations() computes various kinds of theoretical and sample autocorrelations. This is work in progress, see the documentation and vignettes for the current functionality. Function sarima() fits extended multiplicative seasonal ARIMA models with trends, exogenous variables and arbitrary roots on the unit circle, which can be fixed or estimated (for the algebraic basis for this see <arXiv:2208.05055>, a paper on the methodology is being prepared).

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

arima kalman-filter reg-sarima sarima sarimax seasonal time-series xarima openblas cpp

3 stars 6.09 score 112 scripts 1 dependents

sentometricsresearch

sentometrics:An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction

Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.

Maintained by Samuel Borms. Last updated 4 years ago.

nlp prediction sentiment-analysis text-mining time-series openblas cpp openmp

83 stars 6.09 score 49 scripts

ahaeusser

echos:Echo State Networks for Time Series Modeling and Forecasting

Provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

Maintained by Alexander Häußer. Last updated 25 days ago.

echo-state-networks fable fabletools forecast forecasting recurrent-neural-networks reservoir-computing ridge-regression time-series openblas cpp openmp

12 stars 6.03 score 8 scripts

inseefr

disaggR:Two-Steps Benchmarks for Time Series Disaggregation

The twoStepsBenchmark() and threeRuleSmooth() functions allow you to disaggregate a low-frequency time series with higher frequency time series, using the French National Accounts methodology. The aggregated sum of the resulting time series is strictly equal to the low-frequency time series within the benchmarking window. Typically, the low-frequency time series is an annual one, unknown for the last year, and the high frequency one is either quarterly or monthly. See "Methodology of quarterly national accounts", Insee Méthodes N°126, by Insee (2012, ISBN:978-2-11-068613-8, <https://www.insee.fr/en/information/2579410>).

Maintained by Pauline Meinzel. Last updated 9 months ago.

disaggregation statistical-package time-series

11 stars 6.01 score 31 scripts

bioc

timeOmics:Time-Course Multi-Omics data integration

timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step.

Maintained by Antoine Bodein. Last updated 5 months ago.

clustering featureextraction timecourse dimensionreduction software sequencing microarray metabolomics metagenomics proteomics classification regression immunooncology geneprediction multiplecomparison cluster integration multi-omics time-series

24 stars 5.98 score 10 scripts

ropensci

daiquiri:Data Quality Reporting for Temporal Datasets

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

Maintained by T. Phuong Quan. Last updated 7 months ago.

data-quality initial-data-analysis reproducible-research temporal-data time-series

36 stars 5.94 score 12 scripts

justinmshea

neverhpfilter:An Alternative to the Hodrick-Prescott Filter

In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.

Maintained by Justin M. Shea. Last updated 2 years ago.

hamilton hp-filter time-series

14 stars 5.93 score 61 scripts

smeekes

bootUR:Bootstrap Unit Root Tests

Set of functions to perform various bootstrap unit root tests for both individual time series (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>, Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>, Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.

Maintained by Stephan Smeekes. Last updated 2 months ago.

bootstrap dickey-fuller hypothesis-test time-series unit-root openblas cpp

10 stars 5.91 score 27 scripts

agbarnett

season:Seasonal Analysis of Health Data

Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.

Maintained by Adrian Barnett. Last updated 3 years ago.

non-linear seasons time-series

2 stars 5.85 score 70 scripts

prajwalkpatil

VedicDateTime:Vedic Calendar System

Provides platform for Vedic calendar system having several functionalities to facilitate conversion between Gregorian and Vedic calendar systems, and helpful in examining its impact in the time series analysis domain.

Maintained by Neeraj Dhanraj Bokde. Last updated 2 years ago.

calendar panchanga time-series vedic

6 stars 5.84 score 58 scripts

tsmodels

tsmarch:Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Maintained by Alexios Galanos. Last updated 5 days ago.

econometrics finance garch multivariate-timeseries time-series openblas cpp

7 stars 5.80 score 3 scripts

dppalomar

imputeFin:Imputation of Financial Time Series with Missing Values and/or Outliers

Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.

Maintained by Daniel P. Palomar. Last updated 4 years ago.

financial-data missing-values outliers time-series

25 stars 5.80 score 25 scripts

darkeyes

VLTimeCausality:Variable-Lag Time Series Causality Inference Framework

A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.

Maintained by Chainarong Amornbunchornvej. Last updated 10 months ago.

causal-inference granger-causality time-series time-series-analysis transfer-entropy

54 stars 5.77 score 11 scripts

sebkrantz

dfms:Dynamic Factor Models

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Maintained by Sebastian Krantz. Last updated 12 days ago.

dynamic-factor-models time-series openblas cpp

32 stars 5.76 score 12 scripts

rjdverse

rjd3toolkit:Utility Functions around 'JDemetra+ 3.0'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It provides functions allowing to model time series (create outlier regressors, user-defined calendar regressors, UCARIMA models...), to test the presence of trading days or seasonal effects and also to set specifications in pre-adjustment and benchmarking when using rjd3x13 or rjd3tramoseats.

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

java jdemetra seasonal-adjustment time-series timeseries openjdk

6 stars 5.74 score 48 scripts 16 dependents

smac-group

wv:Wavelet Variance

Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

signal-processing time-series wavelet-variance openblas cpp

17 stars 5.73 score 15 scripts 2 dependents

blasbenito

distantia:Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.

Maintained by Blas M. Benito. Last updated 1 months ago.

23 stars 5.73 score 11 scripts

svazzole

sparsevar:Sparse VAR/VECM Models Estimation

A wrapper for sparse VAR/VECM time series models estimation using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped Absolute Deviation) and MCP (Minimax Concave Penalty). Based on the work of Sumanta Basu and George Michailidis <doi:10.1214/15-AOS1315>.

Maintained by Simone Vazzoler. Last updated 4 years ago.

econometrics lasso mcp scad sparse statistics time-series var vecm

11 stars 5.69 score 30 scripts 1 dependents

atsa-es

atsar:Stan Routines For Univariate And Multivariate Time Series

Bundles univariate and multivariate STAN scripts for FISH 507 class.

Maintained by Eric J. Ward. Last updated 10 months ago.

bayesian stan time-series cpp

48 stars 5.68 score 33 scripts

christophsax

seasonalview:Graphical User Interface for Seasonal Adjustment

A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software.

Maintained by Christoph Sax. Last updated 5 months ago.

seasonal-adjustment shiny time-series

22 stars 5.65 score 105 scripts

akai01

caretForecast:Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

Maintained by Resul Akay. Last updated 2 years ago.

caret conformal-prediction data-science econometrics forecast forecasting forecasting-models machine-learning macroeconometrics microeconometrics time-series time-series-forcasting time-series-prediction

25 stars 5.62 score 28 scripts 4 dependents

andyphilips

dynamac:Dynamic Simulation and Testing for Single-Equation ARDL Models

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Maintained by Soren Jordan. Last updated 4 years ago.

ardl stata time-series time-series-analysis

7 stars 5.59 score 37 scripts 1 dependents

ramikrispin

USgas:The Demand for Natural Gas in the US

Provides an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.

Maintained by Rami Krispin. Last updated 2 years ago.

data natural-gas time-series

9 stars 5.56 score 41 scripts

gmgeorg

ForeCA:Forecastable Component Analysis

Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.

Maintained by Georg M. Goerg. Last updated 5 years ago.

blind-source-separation dimensionality-reduction forecasting multivariate-timeseries signal-processing spectrum time-series time-series-analysis

15 stars 5.47 score 39 scripts

vladimirholy

gasmodel:Generalized Autoregressive Score Models

Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>. Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method.

Maintained by Vladimír Holý. Last updated 1 years ago.

dcs gas time-series

14 stars 5.45 score 2 scripts

luisgruber

bayesianVARs:MCMC Estimation of Bayesian Vectorautoregressions

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) <doi:10.48550/arXiv.2206.04902>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.

Maintained by Luis Gruber. Last updated 5 months ago.

bayesian time-series vectorautoregression openblas cpp

9 stars 5.43 score 9 scripts

mlaib

MFDFA:MultiFractal Detrended Fluctuation Analysis

Contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub.

Maintained by Mohamed Laib. Last updated 6 years ago.

multifractal-analysis time-series

17 stars 5.39 score 29 scripts

gitter-lab

LPWC:Lag Penalized Weighted Correlation for Time Series Clustering

Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.

Maintained by Thevaa Chandereng. Last updated 5 years ago.

bioinformatics clustering time-series

20 stars 5.23 score 17 scripts

mfaymon

spINAR:(Semi)Parametric Estimation and Bootstrapping of INAR Models

Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.

Maintained by Maxime Faymonville. Last updated 11 months ago.

bootstrapping count-data parametric-estimation penalization semiparametric-estimation simulation time-series validation

4 stars 5.20 score 7 scripts

marcozanotti

dispositionEffect:Analysis of Disposition Effect on Financial Portfolios

Evaluate the presence of disposition effect and others irrational investor's behaviors based solely on investor's transactions and financial market data. Experimental data can also be used to perform the analysis. Four different methodologies are implemented to account for the different nature of human behaviors on financial markets. Novel analyses such as portfolio driven and time series disposition effect are also allowed.

Maintained by Marco Zanotti. Last updated 3 years ago.

behavioral-economics behavioral-sciences econometrics economics finance financial-analysis financial-data financial-markets time-series

4 stars 5.20 score 9 scripts

haeran-cho

fnets:Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arXiv:2301.11675> accompanying the R package.

Maintained by Haeran Cho. Last updated 4 months ago.

factor-models forecasting high-dimensional network-estimation time-series vector-autoregression cpp

7 stars 5.20 score 28 scripts

macroeconomicdata

dateutils:Date Utils

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

Maintained by Seth Leonard. Last updated 3 years ago.

data-processing econometrics time-series openblas cpp

3 stars 5.17 score 49 scripts

jobnmadu

Dyn4cast:Dynamic Modeling and Machine Learning Environment

Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving.

Maintained by Job Nmadu. Last updated 13 days ago.

data-science equal-lenght-forecast forecasting knots machine-learning nigeria prediction regression-models spline-models statistics time-series

4 stars 5.03 score 38 scripts

dkesada

dbnR:Dynamic Bayesian Network Learning and Inference

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.

Maintained by David Quesada. Last updated 9 months ago.

bayesian-networks dynamic-bayesian-networks forecasting inference time-series cpp

55 stars 5.01 score 37 scripts

ropensci

hydroscoper:Interface to the Greek National Data Bank for Hydrometeorological Information

R interface to the Greek National Data Bank for Hydrological and Meteorological Information. It covers Hydroscope's data sources and provides functions to transliterate, translate and download them into tidy dataframes.

Maintained by Konstantinos Vantas. Last updated 8 months ago.

climate greece hydrology hydrometeorology hydroscope meteorological-data meteorological-stations peer-reviewed tidy-data time-series water-resources

14 stars 4.97 score 33 scripts

hendersontrent

theftdlc:Analyse and Interpret Time Series Features

Provides a suite of functions for analysing, interpreting, and visualising time-series features calculated from different feature sets from the 'theft' package. Implements statistical learning methodologies described in Henderson, T., Bryant, A., and Fulcher, B. (2023) <arXiv:2303.17809>.

Maintained by Trent Henderson. Last updated 2 months ago.

data-science data-visualization machine-learning statistics time-series

4 stars 4.94 score 11 scripts

smac-group

avar:Allan Variance

Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

Maintained by Stéphane Guerrier. Last updated 3 years ago.

allan-variance inertial-sensors statistics time-series cpp

5 stars 4.88 score 9 scripts

tylerjpike

sovereign:State-Dependent Empirical Analysis

A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.

Maintained by Tyler J. Pike. Last updated 2 years ago.

econometrics forecasting impulse-response local-projection macroeconomics state-dependent time-series vector-autoregression

12 stars 4.78 score 8 scripts

alec-stashevsky

blocklength:Select an Optimal Block-Length to Bootstrap Dependent Data (Block Bootstrap)

A set of functions to select the optimal block-length for a dependent bootstrap (block-bootstrap). Includes the Hall, Horowitz, and Jing (1995) <doi:10.1093/biomet/82.3.561> subsampling-based cross-validation method, the Politis and White (2004) <doi:10.1081/ETC-120028836> Spectral Density Plug-in method, including the Patton, Politis, and White (2009) <doi:10.1080/07474930802459016> correction, and the Lahiri, Furukawa, and Lee (2007) <doi:10.1016/j.stamet.2006.08.002> nonparametric plug-in method, with a corresponding set of S3 plot methods.

Maintained by Alec Stashevsky. Last updated 21 days ago.

block-bootstrap block-resampling blocklength boot bootstrap depedent-bootstrap dependent horowitz inference meboot politis resample stats time time-series time-series-analysis tseries

4 stars 4.78 score 8 scripts

robjhyndman

tscompdata:Time series data from various forecasting competitions

Time series data from the following forecasting competitions are provided: M, M3, NN3, NN5, NNGC1, Tourism, and GEFCom2012.

Maintained by Rob Hyndman. Last updated 2 years ago.

data time-series

18 stars 4.69 score 18 scripts

xtreamsrl

tsviz:Easy and Interactive Time Series Visualization

An 'RStudio' add-in to visualize time series. Time series are searched in the global environment as data.frame objects with a column of type date and a column of type numeric. Interactive charts are produced using 'plotly' package.

Maintained by Emanuele Fabbiani. Last updated 4 years ago.

rstudio-addin time-series

45 stars 4.65 score 9 scripts

vandomed

stocks:Stock Market Analysis

Functions for analyzing and visualizing stock market data. Main features are loading and aligning historical data, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs.

Maintained by Dane R. Van Domelen. Last updated 5 years ago.

investment-analysis portfolio-construction portfolio-optimization sharpe-ratio stock-market time-series cpp

22 stars 4.63 score 39 scripts

techtonique

ahead:Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Maintained by T. Moudiki. Last updated 1 months ago.

forecasting machine-learning predictive-modeling statistical-learning time-series time-series-forecasting uncertainty-quantification cpp

21 stars 4.63 score 51 scripts

paulnorthrop

lite:Likelihood-Based Inference for Time Series Extremes

Performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.

Maintained by Paul J. Northrop. Last updated 9 months ago.

clustered extremal-index extreme-value-statistics extremes frequentist generalised-pareto inference likelihood log-likelihood threshold time-series

3 stars 4.56 score 12 scripts

darkeyes

mFLICA:Leadership-Inference Framework for Multivariate Time Series

A leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or 'mFLICA' uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. 'mFLICA' reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.

Maintained by Chainarong Amornbunchornvej. Last updated 10 months ago.

coordination data-science leadership time-series

5 stars 4.54 score 14 scripts

tianshu129

foqat:Field Observation Quick Analysis Toolkit

Tools for quickly processing and analyzing field observation data and air quality data. This tools contain functions that facilitate analysis in atmospheric chemistry (especially in ozone pollution). Some functions of time series are also applicable to other fields. For detail please view homepage<https://github.com/tianshu129/foqat>. Scientific Reference: 1. The Hydroxyl Radical (OH) Reactivity: Roger Atkinson and Janet Arey (2003) <doi:10.1021/cr0206420>. 2. Ozone Formation Potential (OFP): <https://ww2.arb.ca.gov/sites/default/files/classic/regact/2009/mir2009/mir10.pdf>, Zhang et al.(2021) <doi:10.5194/acp-21-11053-2021>. 3. Aerosol Formation Potential (AFP): Wenjing Wu et al. (2016) <doi:10.1016/j.jes.2016.03.025>. 4. TUV model: <https://www2.acom.ucar.edu/modeling/tropospheric-ultraviolet-and-visible-tuv-radiation-model>.

Maintained by Tianshu Chen. Last updated 6 months ago.

air-pollution air-quality air-quality-data air-quality-measurements air-quality-monitor air-quality-reports air-quality-sensor atmospheric-chemistry atmospheric-modelling atmospheric-science daily-maximum-8-hour-ozone field-observation mir ofp ozone-formation-potential photolysis-rate-coefficients time-series time-series-analysis tuv

35 stars 4.54 score 20 scripts

rjdverse

rjd3x13:Seasonal Adjustment with X-13 in 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers full acces to options and outputs of X-13, including RegARIMA modelling (automatic ARIMA model with outlier detection and trading days adjustment) and X-11 decomposition.

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

java jdemetra seasonal-adjustment time-series timeseries x13 openjdk

5 stars 4.48 score 8 scripts 4 dependents

jstriaukas

midasml:Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data

The 'midasml' package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the 'midasml' approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.

Maintained by Jonas Striaukas. Last updated 2 years ago.

forecasting-models machine-learning nowcasting-models sparse-group-lasso time-series fortran

37 stars 4.39 score 5 scripts

rjdverse

rjd3tramoseats:Seasonal Adjustment with TRAMO-SEATS in 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers full acces to options and outputs of TRAMO-SEATS (Time series Regression with ARIMA noise, Missing values and Outliers - Signal Extraction in ARIMA Time Series), including TRAMO modelling (automatic ARIMA model with outlier detection and trading days adjustment).

Maintained by Tanguy Barthelemy. Last updated 5 months ago.

java jdemetra seasonal-adjustment time-series timeseries tramoseats openjdk

5 stars 4.33 score 12 scripts 3 dependents

robson-fernandes

dbnlearn:Dynamic Bayesian Network Structure Learning, Parameter Learning and Forecasting

It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

Maintained by Robson Fernandes. Last updated 5 years ago.

bayesian-inference bayesian-networks dynamic-bayesian-networks probabilistic-graphical-models time-series

16 stars 4.32 score 26 scripts

kvasilopoulos

transx:Transform Univariate Time Series

Univariate time series operations that follow an opinionated design. The main principle of 'transx' is to keep the number of observations the same. Operations that reduce this number have to fill the observations gap.

Maintained by Kostas Vasilopoulos. Last updated 4 years ago.

detrend filters outliers time-series transx

3 stars 4.29 score 13 scripts

ethanbass

VPdtw:Variable Penalty Dynamic Time Warping

Variable Penalty Dynamic Time Warping (VPdtw) for aligning chromatographic signals. With an appropriate penalty this method performs good alignment of chromatographic data without deforming the peaks (Clifford, D., Stone, G., Montoliu, I., Rezzi S., Martin F., Guy P., Bruce S., and Kochhar S.(2009) <doi:10.1021/ac802041e>; Clifford, D. and Stone, G. (2012) <doi:10.18637/jss.v047.i08>).

Maintained by Ethan Bass. Last updated 7 months ago.

chemoinformatics chemometrics chromatography time-series time-warping cpp

2 stars 4.26 score 2 scripts 2 dependents

roga11

MSTest:Hypothesis Testing for Markov Switching Models

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2024_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20241015.pdf> that can be used to identify the number of regimes in Markov switching models.

Maintained by Gabriel Rodriguez Rondon. Last updated 1 months ago.

autoregressive bootstrap hypothesis-testing likelihood-ratio-test markov-chain moments monte-carlo non-linear regime-switching time-series openblas cpp openmp

5 stars 4.18 score 3 scripts

geobosh

pcts:Periodically Correlated and Periodically Integrated Time Series

Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996) <doi:10.1111/j.1467-9892.1996.tb00281.x>.

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

par-models periodic periodic-models piar-models seasonal time-series time-series-models

3 stars 4.18 score 3 scripts

geobosh

mcompanion:Objects and Methods for Multi-Companion Matrices

Provides a class for multi-companion matrices with methods for arithmetic and factorization. A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>.

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

eigen-vector-decomposition matrices periodic time-series

4.05 score 37 scripts 2 dependents

atsa-es

tvvarss:Time Varying Vector Autoregressive State Space Models

The tvvarss package uses Stan (mc-stan.org) to fit multi-site multivariate autoregressive (aka vector autoregressive) state space models with a time varying interaction matrix.

Maintained by Eric Ward. Last updated 3 years ago.

bayesian multivariate-timeseries state-space time-series cpp

10 stars 4.04 score 11 scripts

christophsax

dataseries:Switzerland's Data Series in One Place

Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.

Maintained by Christoph Sax. Last updated 1 years ago.

time-series

9 stars 4.01 score 23 scripts

alobondo

DeductiveR:Deductive Rational Method

Apply the Deductive Rational Method to a monthly series of flow or precipitation data to fill in missing data. The method is as described in: Campos, D.F., (1984, ISBN:9686194444).

Maintained by Alonso Arriagada. Last updated 3 months ago.

hydrology statistics time-series

2 stars 4.00 score 2 scripts

spkaluzny

splusTimeSeries:Time Series from 'S-PLUS'

A collection of classes and methods for working with indexed rectangular data. The index values can be calendar (timeSeries class) or numeric (signalSeries class). Methods are included for aggregation, alignment, merging, and summaries. The code was originally available in 'S-PLUS'.

Maintained by Stephen Kaluzny. Last updated 6 months ago.

data-structures time-series

3.95 score 20 scripts 1 dependents

alsabtay

ATAforecasting:Automatic Time Series Analysis and Forecasting using the Ata Method

The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan’s PhD dissertation.

Maintained by Ali Sabri Taylan. Last updated 2 years ago.

ata ataforecasting fable forecast forecasting time-series cpp

5 stars 3.88 score 4 scripts 1 dependents

jeswheel

arima2:Likelihood Based Inference for ARIMA Modeling

Estimating and analyzing auto regressive integrated moving average (ARIMA) models. The primary function in this package is arima(), which fits an ARIMA model to univariate time series data using a random restart algorithm. This approach frequently leads to models that have model likelihood greater than or equal to that of the likelihood obtained by fitting the same model using the arima() function from the 'stats' package. This package enables proper optimization of model likelihoods, which is a necessary condition for performing likelihood ratio tests. This package relies heavily on the source code of the arima() function of the 'stats' package. For more information, please see Jesse Wheeler and Edward L. Ionides (2023) <arXiv:2310.01198>.

Maintained by Jesse Wheeler. Last updated 9 months ago.

arima arma maximum-likelihood-estimation time-series time-series-analysis

3 stars 3.86 score 12 scripts

lehmasve

hdflex:High-Dimensional Aggregate Density Forecasts

Provides a forecasting method that efficiently maps vast numbers of (scalar-valued) signals into an aggregate density forecast in a time-varying and computationally fast manner. The method proceeds in two steps: First, it transforms a predictive signal into a density forecast and, second, it combines the resulting candidate density forecasts into an ultimate aggregate density forecast. For a detailed explanation of the method, please refer to Adaemmer et al. (2023) <doi:10.2139/ssrn.4342487>.

Maintained by Sven Lehmann. Last updated 5 months ago.

ensemble-learning forecast-combination forecasting high-dimensionality time-series openblas cpp openmp

3 stars 3.78 score 1 scripts

matrix-profile-foundation

matrixprofiler:Matrix Profile for R

This is the core functions needed by the 'tsmp' package. The low level and carefully checked mathematical functions are here. These are implementations of the Matrix Profile concept that was created by CS-UCR <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.

Maintained by Francisco Bischoff. Last updated 3 years ago.

algorithm matrix-profile rcpp time-series cpp

10 stars 3.70 score 2 scripts

akai01

ngboostForecast:Probabilistic Time Series Forecasting

Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

Maintained by Resul Akay. Last updated 3 years ago.

forecasting machine-learning ngboost ngboost-forecast probabilistic-forecasts python sklearn time-series

7 stars 3.69 score 14 scripts

atsa-es

marssTMB:Fast fitting of MARSS models with TMB

Companion to the MARSS package. Fast fitting of MARSS models with TMB. See the MARSS documentation. All the model syntax and features are the same as for the MARSS package.

Maintained by Elizabeth E. Holmes. Last updated 2 years ago.

marss multivariate-timeseries state-space-model time-series tmb

1 stars 3.68 score 19 scripts

euanmcgonigle

CptNonPar:Nonparametric Change Point Detection for Multivariate Time Series

Implements the nonparametric moving sum procedure for detecting changes in the joint characteristic function (NP-MOJO) for multiple change point detection in multivariate time series. See McGonigle, E. T., Cho, H. (2023) <doi:10.48550/arXiv.2305.07581> for description of the NP-MOJO methodology.

Maintained by Euan T. McGonigle. Last updated 11 months ago.

change-point-detection moving-sum nonparametric segmentation time-series cpp

4 stars 3.60 score 4 scripts

rkbauer

RchivalTag:Analyzing and Interactive Visualization of Archival Tagging Data

A set of functions to generate, access and analyze standard data products from archival tagging data.

Maintained by Robert K. Bauer. Last updated 2 months ago.

data-visuali depth depth-temperature-profiles dygraphs ggpot leaflet minipat pelagic plotly satellite sensor spatial star-oddi temperature time-series tracks wildlife-computers

1 stars 3.59 score 26 scripts

enricoschumann

tsdb:Terribly-Simple Data Base for Time Series

A terribly-simple data base for numeric time series, written purely in R, so no external database-software is needed. Series are stored in plain-text files (the most-portable and enduring file type) in CSV format. Timestamps are encoded using R's native numeric representation for 'Date'/'POSIXct', which makes them fast to parse, but keeps them accessible with other software. The package provides tools for saving and updating series in this standardised format, for retrieving and joining data, for summarising files and directories, and for coercing series from and to other data types (such as 'zoo' series).

Maintained by Enrico Schumann. Last updated 9 days ago.

csv time-series tsdb

11 stars 3.52 score

euanmcgonigle

TrendLSW:Wavelet Methods for Analysing Locally Stationary Time Series

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will likely want to start with the TLSW function.

Maintained by Euan T. McGonigle. Last updated 11 months ago.

nonparametric-regression spectral-analysis spectrum time-series time-series-analysis wavelets

1 stars 3.30 score 3 scripts

bips-hb

HMMpa:Analysing Accelerometer Data Using Hidden Markov Models

Analysing time-series accelerometer data to quantify length and intensity of physical activity using hidden Markov models. It also contains the traditional cut-off point method. Witowski V, Foraita R, Pitsiladis Y, Pigeot I, Wirsik N (2014). <doi:10.1371/journal.pone.0114089>.

Maintained by Foraita Ronja. Last updated 2 months ago.

accelerometer-data hidden-markov-model time-series

3.18 score 1 dependents

atsa-es

mvdlm:Multivariate Dynamic Linear Modelling With Stan

Fits multivariate dynamic linear models in a Bayesian framework using Stan.

Maintained by Eric J. Ward. Last updated 10 months ago.

bayesian dlm stan time-series cpp

1 stars 3.18 score 3 scripts

atsa-es

varlasso:Vector Autoregressive State Space Models With Shrinkage

The varlasso package uses Stan (mc-stan.org) to fit VAR state space models with optional shrinkage priors on B matrix elements (autoregression coefficients).

Maintained by Eric Ward. Last updated 2 years ago.

bayesian multivariate-timeseries time-series cpp

2 stars 3.00 score 2 scripts

azalk

RChest:Locating Distributional Changes in Highly Dependent Time Series

Provides algorithms to locate multiple distributional change-points in piecewise stationary time series. The algorithms are provably consistent, even in the presence of long-range dependencies. Knowledge of the number of change-points is not required. The code is written in Go and interfaced with R.

Maintained by Lukas Zierahn. Last updated 3 years ago.

changepoints consistent ergodic long-range-dependence stationary time-series

2 stars 3.00 score 1 scripts

atsa-es

MAR1:Multivariate Autoregressive Modeling for Analysis of Community Time-Series Data

The MAR1 package provides basic tools for preparing ecological community time-series data for MAR modeling, building MAR-1 models via model selection and bootstrapping, and visualizing and exporting model results. It is intended to make MAR analysis sensu Ives et al. (2003) Analysis of community stability and ecological interactions from time-series data) a more accessible tool for anyone studying community dynamics. The user need not necessarily be familiar with time-series modeling or command-based statistics programs such as R.

Maintained by Elizabeth Eli Holmes. Last updated 2 years ago.

multivariate-timeseries time-series

1 stars 3.00 score

gregorkb

QregBB:Block Bootstrap Methods for Quantile Regression in Time Series

Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.

Maintained by Karl Gregory. Last updated 3 years ago.

bootstrap quantile-regression time-series

2 stars 3.00 score 1 scripts

geobosh

mixAR:Mixture Autoregressive Models

Model time series using mixture autoregressive (MAR) models. Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) <doi:10.1111/1467-9868.00222>, Boshnakov (2009) <doi:10.1016/j.spl.2009.04.009>), and the extensive references in the documentation.

Maintained by Georgi N. Boshnakov. Last updated 5 months ago.

assymetric heteroskedasticity mixture-autoregressive student-t time-series

1 stars 2.70 score 6 scripts

mtrupiano1

knnwtsim:K Nearest Neighbor Forecasting with a Tailored Similarity Metric

Functions to implement K Nearest Neighbor forecasting using a weighted similarity metric tailored to the problem of forecasting univariate time series where recent observations, seasonal patterns, and exogenous predictors are all relevant in predicting future observations of the series in question. For more information on the formulation of this similarity metric please see Trupiano (2021) <arXiv:2112.06266>.

Maintained by Matthew Trupiano. Last updated 3 years ago.

forecasting knn-regression machine-learning time-series

1 stars 2.70 score 2 scripts

christophergandrud

pltesim:Simulate Probabilistic Long-Term Effects in Models with Temporal Dependence

Calculates and depicts probabilistic long-term effects in binary models with temporal dependence variables. The package performs two tasks. First, it calculates the change in the probability of the event occurring given a change in a theoretical variable. Second, it calculates the rolling difference in the future probability of the event for two scenarios: one where the event occurred at a given time and one where the event does not occur. The package is consistent with the recent movement to depict meaningful and easy-to-interpret quantities of interest with the requisite measures of uncertainty. It is the first to make it easy for researchers to interpret short- and long-term effects of explanatory variables in binary autoregressive models, which can have important implications for the correct interpretation of these models.

Maintained by Christopher Gandrud. Last updated 8 years ago.

simulation time-series visualization

1 stars 2.70 score 6 scripts