Meta-learning how to forecast time series

Thiyanga S. Talagala, Rob J. Hyndman, George Athanasopoulos

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1 Citation (Scopus)


Features of time series are useful in identifying suitable models for forecasting. We present a general framework, labelled Feature-based FORecast Model Selection (FFORMS), which selects forecast models based on features calculated from each time series. The FFORMS framework builds a mapping that relates the features of a time series to the “best” forecast model using a classification algorithm such as a random forest. The framework is evaluated using time series from the M-forecasting competitions and is shown to yield forecasts that are almost as accurate as state-of-the-art methods but are much faster to compute. We use model-agnostic machine learning interpretability methods to explore the results and to study what types of time series are best suited to each forecasting model.

Original languageEnglish
Pages (from-to)1476-1501
Number of pages26
JournalJournal of Forecasting
Issue number6
Publication statusPublished - Sept 2023


  • algorithm selection problem
  • black-box models
  • machine learning interpretability
  • random forest
  • visualization

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