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FFORMA: Feature-based forecast model averaging
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DOI：10.1016/j.ijforecast.2019.02.011.png)
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Abstract
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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. (C) 2019 Published by Elsevier B.V. on behalf of International Institute of Forecasters.
Keywords:
Time series features
Forecast combination
XGBoost
M4 competition
Meta-learning
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