11.6 Further reading

  • De Livera et al. (2011) introduced the TBATS model and discuss the problem of complex seasonality in general.
  • Pfaff (2008) provides a book-length overview of VAR modelling and other multivariate time series models.
  • Neural networks for individual time series have not tended to produce good forecasts. Crone, Hibon, & Nikolopoulos (2011) discuss this issue in the context of a forecasting competition.
  • 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.


De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. J American Statistical Association, 106(496), 1513–1527. https://robjhyndman.com/publications/complex-seasonality/

Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. New York, USA: Springer Science & Business Media. [Amazon]

Crone, S. F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. https://doi.org/10.1016/j.ijforecast.2011.04.001

Lahiri, S. N. (2003). Resampling methods for dependent data. New York, USA: Springer Science & Business Media. [Amazon]

Bergmeir, C., Hyndman, R. J., & Ben’itez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303–312. https://robjhyndman.com/publications/bagging-ets/