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Hierarchical and grouped time series

This package presents functions to create, plot and forecast hierarchical and grouped time series. In forecasting hierarchical and grouped time series, the base methods implemented include ETS, ARIMA and the naive (random walk) models. Forecasts for grouped time series are calibrated using bottom-up and optimal combination methods. Forecasts for hierarchical time series are distributed in the hierarchy using bottom-up, top-down, middle-out and optimal combination methods. Three top-down methods are available: the two Gross-Sohl methods and the forecast-proportion approach of Hyndman, Ahmed, and Athanasopoulos (2011).

Keywords
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References

G. Athanasopoulos, R. A. Ahmed and R. J. Hyndman (2009) Hierarchical forecasts for Australian domestic tourism, International Journal of Forecasting, 25, 146-166.

R. J. Hyndman, R. A. Ahmed, G. Athanasopoulos and H.L. Shang (2011) Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579--2589. http://robjhyndman.com/papers/hierarchical/

Hyndman, R. J., Lee, A., & Wang, E. (2016). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16-23. https://robjhyndman.com/papers/hgts7.pdf

Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2018). Forecasting hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, to appear https://robjhyndman.com/papers/mint.pdf

Aliases
  • hts-package
Documentation reproduced from package hts, version 6.0.1, License: GPL (>= 2)

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