12.7 Further reading

  • The Prophet model is described in S. J. Taylor & Letham (2018).
  • Pfaff (2008) provides a book-length overview of VAR modelling and other multivariate time series models.
  • A current survey of the use of recurrent neural networks for forecasting is provided by Hewamalage et al. (2021).
  • Bootstrapping for time series is discussed in Lahiri (2003).
  • Bagging for time series forecasting is relatively new. Bergmeir et al. (2016) is one of the few papers which addresses this topic.

Bibliography

Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303–312. https://doi.org/10.1016/j.ijforecast.2015.07.002
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. https://doi.org/10.1016/j.ijforecast.2020.06.008
Lahiri, S. N. (2003). Resampling methods for dependent data. Springer Science & Business Media. http://amazon.com/dp/0387009280
Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. Springer Science & Business Media. http://amazon.com/dp/0387759662
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. https://doi.org/10.1080/00031305.2017.1380080