Time Series Forecasting Based on Multiscale Fusion Transformer in Finance

Abstract

Time series forecasting is significant in market research and decision-making in the financial sector, but the complexity and uncertainty of financial data pose challenges to accurate forecasting. Although deep learning methods, including transformers, have significantly improved the forecasting effect, these methods still have limitations in dealing with the multiscale features of financial time series and their complex serial correlation. They fail to fully utilize the frequency domain’s multiscale features and spatial relationships. For this situation, this study proposes a time series forecasting method based on the multiscale fusion transformer for financial data, which aims to extract significant periodic patterns using frequency domain analysis effectively. Besides, the multiscale attention mechanism and graph convolution module are introduced to realize the detailed modeling of the time series simultaneously, effectively capture the spatial relationship, and obtain the correlation between different series on multiple frequency scales. In this study, experimental validation is carried out on several financial time series datasets, and the findings demonstrate that the proposed approach positively impacts predicting accuracy.