2021/08/04 by Li Li, Yanfei Kang, Li, Li +3 · 2 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Computation (stat.CO) #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Forecasting Techniques and Applications #Stock Market Forecasting Methods #Time Series Analysis and Forecasting #econ.EM #stat.CO
paper · pdf · doi:10.48550/arxiv.2108.02082
openalex publication_date 2021/08/04 · arxiv created 2022/06/14 · arxiv updated 2022/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we propose a novel framework for density forecast combination by constructing time-varying weights based on time series features, which is called Feature-based Bayesian Forecasting Model Averaging (FEBAMA). Our framework estimates weights in the forecast combination via Bayesian log predictive scores, in which the optimal forecasting combination is determined by time series features from historical information. In particular, we use an automatic Bayesian variable selection method to add weight to 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 and density forecasts.