.png){kind=icon}
.png){kind=icon}
Return
Bayesian forecast combination using time-varying features
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijforecast.2022.06.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.& COPY; 2022 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keywords:
Forecast combination
Bayesian density forecasting
Time -varying features
Log predictive score
Interpretability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
3.1K
Citations:
9.9K