Meta‐learning how to forecast time series

作者：Thiyanga S. Talagala, Rob J. Hyndman, George A. Athanasopoulos · 发表于：Journal of Forecasting · 年份：2023 · DOI：10.1002/for.2963 · 被引用次数：110 · 研究领域：Forecasting Techniques and Applications、Time Series Analysis and Forecasting、Stock Market Forecasting Methods

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.