Multi-period Learning for Financial Time Series Forecasting

TL;DR

This paper introduces a Multi-period Learning Framework (MLF) that improves financial time series forecasting by effectively integrating multi-period inputs through novel modules, enhancing accuracy and efficiency.

Contribution

The paper presents a new MLF with three modules for better multi-period input integration and a patch squeeze method to boost efficiency, addressing limitations of existing models.

Findings

Enhanced forecasting accuracy with multi-period inputs.

Reduced redundancy and bias in multi-period data processing.

Improved efficiency through patch squeezing.

Abstract

Time series forecasting is important in finance domain. Financial time series (TS) patterns are influenced by both short-term public opinions and medium-/long-term policy and market trends. Hence, processing multi-period inputs becomes crucial for accurate financial time series forecasting (TSF). However, current TSF models either use only single-period input, or lack customized designs for addressing multi-period characteristics. In this paper, we propose a Multi-period Learning Framework (MLF) to enhance financial TSF performance. MLF considers both TSF's accuracy and efficiency requirements. Specifically, we design three new modules to better integrate the multi-period inputs for improving accuracy: (i) Inter-period Redundancy Filtering (IRF), that removes the information redundancy between periods for accurate self-attention modeling, (ii) Learnable Weighted-average Integration (LWI), that effectively integrates multi-period forecasts, (iii) Multi-period self-Adaptive Patching (MAP), that mitigates the bias towards certain periods by setting the same number of patches across all periods. Furthermore, we propose a Patch Squeeze module to reduce the number of patches in self-attention modeling for maximized efficiency. MLF incorporates multiple inputs with varying lengths (periods) to achieve better accuracy and reduces the costs of selecting input lengths during training. The codes and datasets are available at https://github.com/Meteor-Stars/MLF.