bayesforecast-package: Bayesian Time Series Modeling with 'Stan'.
In bayesforecast: Bayesian Time Series Modeling with Stan
Description
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.
References
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: https://mc-stan.org.
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.
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Description
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.
References
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: https://mc-stan.org.
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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