 FFORMA: Feature-based forecast model averagingPablo Montero-Manso, George Athanasopoulos (), Rob Hyndman and Thiyanga S. Talagala International Journal of Forecasting, 2020, vol. 36, issue 1, 86-92 Abstract: We propose an automated method for obtaining weighted forecast combinations using time series features. The proposed approach involves two phases. First, we use a collection of time series to train a meta-model for assigning weights to various possible forecasting methods with the goal of minimizing the average forecasting loss obtained from a weighted forecast combination. The inputs to the meta-model are features that are extracted from each series. Then, in the second phase, we forecast new series using a weighted forecast combination, where the weights are obtained from our previously trained meta-model. Our method outperforms a simple forecast combination, as well as all of the most popular individual methods in the time series forecasting literature. The approach achieved second position in the M4 competition. Keywords: Time series features; Forecast combination; XGBoost; M4 competition; Meta-learning (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:1:p:86-92 DOI: 10.1016/j.ijforecast.2019.02.011 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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