Bayesian forecast combination using time-varying features

TL;DR

This paper introduces FEBAMA, a Bayesian framework that dynamically combines forecasts using time series features, improving accuracy and interpretability over traditional methods.

Contribution

The paper presents a novel feature-based Bayesian forecast combination method with automatic feature selection, enhancing interpretability and forecast accuracy.

Findings

FEBAMA outperforms benchmark methods in stock and M3 data.

Bayesian variable selection improves forecast accuracy.

The approach offers better interpretability than black-box models.

Abstract

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.