Meta‐learning how to forecast time series

Thiyanga S. Talagala · Rob J. Hyndman

110 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

Abstract

Abstract 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.

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n.d.Forecasting principles and practice 2000The M3-Competition: results, conclusions and implications 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 2002A state space framework for automatic forecasting using exponential smoothing methods 2008Forecasting with Exponential Smoothing 2019The M4 Competition: 100,000 time series and 61 forecasting methods 2017Automatic time series forecasting: the forecast package for R 1989Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource

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