bayesforecast-package: Bayesian Time Series Modeling with 'Stan'.

Description References


Fit univariate 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, and stochastic volatility models. 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.


Carpenter, B. and Gelman, A. and Hoffman, D. and Lee, D. and Goodrich, B. and Betancourt, M. and Brubaker, and Guo, L. and Riddell. 2017. Stan: A probabilistic programming language. Journal of Statistical Software 76(1). doi: 10.18637/jss.v076.i01.

Stan Development Team. (2018). Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0. url:

Hyndman, R. & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software. 26(3), 1-22.doi: 10.18637/jss.v027.i03.

Tsay, R (2010). Analysis of Financial Time Series. Wiley-Interscience. 978-0470414354, second edition.

Shumway, R.H. and Stoffer, D.S. (2010).Time Series Analysis and Its Applications: With R Examples. Springer Texts in Statistics. isbn: 9781441978646. First edition.

bayesforecast documentation built on June 17, 2021, 5:14 p.m.