R-universe search: topic:timeseries

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

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

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

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 15 hours ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

148 stars 9.92 score 117 scripts

eblondel

rsdmx:Tools for Reading SDMX Data and Metadata

Set of classes and methods to read data and metadata documents exchanged through the Statistical Data and Metadata Exchange (SDMX) framework, currently focusing on the SDMX XML standard format (SDMX-ML).

Maintained by Emmanuel Blondel. Last updated 9 days ago.

api datastructures dsd read readsdmx sdmx sdmx-format sdmx-provider sdmx-standards statistics timeseries web-services

105 stars 9.37 score 4 dependents

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

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

reconhub

aweek:Convert Dates to Arbitrary Week Definitions

Which day a week starts depends heavily on the either the local or professional context. This package is designed to be a lightweight solution to easily switching between week-based date definitions.

Maintained by Zhian N. Kamvar. Last updated 4 years ago.

date-formatting dates epidemiology timeseries week

17 stars 7.91 score 117 scripts 14 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

idsia

bayesRecon:Probabilistic Reconciliation via Conditioning

Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted).

Maintained by Dario Azzimonti. Last updated 2 months ago.

reconciliation timeseries

6 stars 7.06 score 40 scripts

doi-usgs

ncdfgeom:'NetCDF' Geometry and Time Series

Tools to create time series and geometry 'NetCDF' files.

Maintained by David Blodgett. Last updated 4 months ago.

cf-conventions geometry netcdf timeseries timeseries-data

18 stars 6.96 score 11 scripts 3 dependents

tsmodels

tsdistributions:Location Scale Standardized Distributions

Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.

Maintained by Alexios Galanos. Last updated 5 months ago.

distributions finance probability-distribution probability-distributions statistical-distributions timeseries cpp

4 stars 6.66 score 19 scripts 2 dependents

dleutnant

influxdbr:R Interface to InfluxDB

An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.

Maintained by Dominik Leutnant. Last updated 5 years ago.

database influxdb tidyverse timeseries

94 stars 5.90 score 28 scripts

yjunechoe

jlmerclusterperm:Cluster-Based Permutation Analysis for Densely Sampled Time Data

An implementation of fast cluster-based permutation analysis (CPA) for densely-sampled time data developed in Maris & Oostenveld, 2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized, mixed-effects) regression models for the calculation of timewise statistics. Provides both a wholesale and a piecemeal interface to the CPA procedure with an emphasis on interpretability and diagnostics. Integrates 'Julia' libraries 'MixedModels.jl' and 'GLM.jl' for performance improvements, with additional functionalities for interfacing with 'Julia' from 'R' powered by the 'JuliaConnectoR' package.

Maintained by June Choe. Last updated 19 days ago.

cluster-based-permutation-test eeg eyetracking mixed-effects-models timeseries

13 stars 5.86 score 14 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

mazamascience

MazamaTimeSeries:Core Functionality for Environmental Time Series

Utility functions for working with environmental time series data from known locations. The compact data model is structured as a list with two dataframes. A 'meta' dataframe contains spatial and measuring device metadata associated with deployments at known locations. A 'data' dataframe contains a 'datetime' column followed by columns of measurements associated with each "device-deployment". Ephemerides calculations are based on code originally found in NOAA's "Solar Calculator" <https://gml.noaa.gov/grad/solcalc/>.

Maintained by Jonathan Callahan. Last updated 1 years ago.

timeseries

5.27 score 62 scripts 1 dependents

jmotif

jmotif:Time Series Analysis Toolkit Based on Symbolic Aggregate Discretization, i.e. SAX

Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly), and characteristic pattern discovery along with interpretable time series classification.

Maintained by Pavel Senin. Last updated 3 years ago.

anomalydiscovery discord discretization kdd sax sax-vsm timeseries cpp

55 stars 5.12 score 48 scripts

marksendak

constellation:Identify Event Sequences Using Time Series Joins

Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.

Maintained by Mark Sendak. Last updated 6 years ago.

electronic-health-record electronic-health-records healthcare patients timeseries

6 stars 4.76 score 19 scripts

ropensci

butterfly:Verification For Continually Updating Timeseries Data

Verification of continually updating timeseries data where we expect new values, but want to ensure previous data remains unchanged.

Maintained by Thomas Zwagerman. Last updated 3 days ago.

data-versioning qaqc timeseries verification

7 stars 4.66 score

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

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

domodwyer

promr:Prometheus 'PromQL' Query Client for 'R'

A native 'R' client library for querying the 'Prometheus' time-series database, using the 'PromQL' query language.

Maintained by Dom Dwyer. Last updated 3 years ago.

metrics prometheus timeseries

9 stars 3.69 score 11 scripts

tractortom

TractorTsbox:Wrangle and Modify Ts Object with Classic Frequencies and Exact Dates Manipuler et Modifier Les Objets Ts Avec Des Fréquences Classiques et Des Dates Exactes

The `ts` objects in R are managed using a very specific date format (in the form `c(2022, 9)` for September 2022 or `c(2021, 2)` for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of `ts` objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic stats functions for creating and managing time series (`ts()`, `window()`). Les objets `ts` en R sont gérés par un format de date très particulier (sous la forme `c(2022, 9)` pour septembre 2022 ou `c(2021, 2)` pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets `ts`. L'idée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. C'est un compromis entre simplicité, précision et utilisation des fonctions du package {stats} de création et de gestion des séries temporelles (`ts()`, `window()`).

Maintained by Tanguy Barthelemy. Last updated 5 days ago.

date datetime timeseries ts

2.54 score