Meta‐learning how to forecast time series

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 re...

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Bibliographic Details
Published in:Journal of forecasting Vol. 42; no. 6; pp. 1476 - 1501
Main Authors: Talagala, Thiyanga S., Hyndman, Rob J., Athanasopoulos, George
Format: Journal Article
Language:English
Published: Chichester Wiley Periodicals Inc 01.09.2023
Subjects:
Agnosticism
algorithm selection problem
black‐box models
Classification
Forecasting
Machine learning
machine learning interpretability
Mapping
random forest
Time
Time series
visualization
ISSN:0277-6693, 1099-131X
Online Access:Get full text
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Summary: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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ISSN:0277-6693
1099-131X
DOI:10.1002/for.2963