FFORMA: Feature-based forecast model averaging

Pablo Montero-Manso (p.montero.manso@udc.es), George Athanasopoulos (george.athanasopoulos@monash.edu), Rob Hyndman and Thiyanga Talagala (thiyanga.talagala@monash.edu)

No 19/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

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 to assign 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 extracted from each series. 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, and outperforms 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 feature; forecast combination; XGBoost; M4 competition; meta-learning. (search for similar items in EconPapers)
JEL-codes: C10 C14 C22 (search for similar items in EconPapers)
Pages: 9
Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: FFORMA: Feature-based forecast model averaging (2020)
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