R-universe search: sarima

Showing 12 of total 12 results (show query)

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 12 months ago.

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

103.9 match 3 stars 6.71 score 112 scripts 1 dependents

dsstoffer

astsa:Applied Statistical Time Series Analysis

Contains data sets and scripts for analyzing time series in both the frequency and time domains including state space modeling as well as supporting the texts Time Series Analysis and Its Applications: With R Examples (5th ed), by R.H. Shumway and D.S. Stoffer. Springer Texts in Statistics, 2025, <https://link.springer.com/book/9783031705830>, and Time Series: A Data Analysis Approach Using R. Chapman-Hall, 2019, <DOI:10.1201/9780429273285>.

Maintained by David Stoffer. Last updated 2 months ago.

8.6 match 7 stars 7.88 score 2.2k scripts 8 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

6.9 match 15 stars 7.68 score 59 scripts 4 dependents

asael697

bayesforecast:Bayesian Time Series Modeling with Stan

Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.

bayesian-inference forecasting-models mcmc stan time-series-analysis cpp

7.6 match 45 stars 6.92 score 62 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.

jdemetra seasonal-adjustment timeseries openjdk

5.9 match 5 stars 5.81 score 48 scripts 15 dependents

rjdverse

rjd3sts:State Space Framework and Structural Time Series with 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.

Maintained by Jean Palate. Last updated 8 months ago.

openjdk

4.8 match 2 stars 6.64 score 25 scripts 4 dependents

vivienroussez

autoTS:Automatic Model Selection and Prediction for Univariate Time Series

Offers a set of functions to easily make predictions for univariate time series. 'autoTS' is a wrapper of existing functions of the 'forecast' and 'prophet' packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

Maintained by Vivien Roussez. Last updated 5 years ago.

4.8 match 10 stars 4.78 score 12 scripts

obriet

gsarima:Two Functions for Generalized SARIMA Time Series Simulation

Write SARIMA models in (finite) AR representation and simulate generalized multiplicative seasonal autoregressive moving average (time) series with Normal / Gaussian, Poisson or negative binomial distribution. The methodology of this method is described in Briet OJT, Amerasinghe PH, and Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.

Maintained by Olivier Briet. Last updated 5 years ago.

6.9 match 1.78 score 3 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 11 months ago.

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

1.7 match 76 stars 6.98 score 25 scripts

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

1.3 match 15 stars 6.14 score 37 scripts

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 2 days ago.

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

0.5 match 90 stars 11.87 score 412 scripts 25 dependents

aqlt

rjd3report:Quality Assessment and Reportiing for Seasonal Adjustment

Add-in to the 'RJDemetra' package on seasonal adjustments. It allows to produce quality assessments outputs (such as dashboards).

Maintained by Alain Quartier-la-Tente. Last updated 4 months ago.

openjdk

2.0 match 1 stars 2.30 score 2 scripts