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) ; Carpenter et al. (2017) .


Reference manual

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1.0.1 by Asael Alonzo Matamoros, 7 months ago

Browse source code at

Authors: Asael Alonzo Matamoros [aut, cre] , Cristian Cruz Torres [aut] , Andres Dala [ctb] , Rob Hyndman [ctb] , Mitchell O'Hara-Wild [ctb]

Documentation:   PDF Manual  

Task views: Time Series Analysis

GPL-2 license

Imports bayesplot, methods, gridExtra, ggplot2, forecast, loo, Rcpp, rstan, rstantools, RcppParallel, bridgesampling, MASS, StanHeaders, astsa, lubridate, prophet, zoo

Suggests knitr, rmarkdown, ggfortify

Linking to BH, Rcpp, RcppParallel, RcppEigen, rstan, StanHeaders

System requirements: GNU make

Depended on by bayesmodels.

See at CRAN