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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Veröffentlicht in:	Journal of forecasting Jg. 42; H. 6; S. 1476 - 1501
Hauptverfasser:	Talagala, Thiyanga S., Hyndman, Rob J., Athanasopoulos, George
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester Wiley Periodicals Inc 01.09.2023
Schlagworte:	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-Zugang:	Volltext
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