Bayesian forecast combination using time-varying features

Li Li, Yanfei Kang and Feng Li ()

International Journal of Forecasting, 2023, vol. 39, issue 3, 1287-1302

Abstract: In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time-varying features. Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecast combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to identify the importance of different features. To this end, our approach has better interpretability compared to other black-box forecasting combination schemes. We apply our framework to stock market data and M3 competition data. Based on our structure, a simple maximum-a-posteriori scheme outperforms benchmark methods, and Bayesian variable selection can further enhance the accuracy for both point forecasts and density forecasts.

Keywords: Forecast combination; Bayesian density forecasting; Time-varying features; Log predictive score; Interpretability (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Working Paper: Bayesian forecast combination using time-varying features (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:39:y:2023:i:3:p:1287-1302

DOI: 10.1016/j.ijforecast.2022.06.002

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