Multi-source Transfer Learning with Ensemble for Financial Time Series Forecasting

TL;DR

This paper explores multi-source transfer learning methods for financial time series forecasting, proposing two novel ensemble techniques that outperform baselines on stock market data.

Contribution

It introduces two new multi-source transfer learning methods, WAETL and TPEES, tailored for financial time series forecasting, addressing the limitations of single-source transfer learning.

Findings

TPEES outperforms baseline methods in most transfer tasks.

Multi-source transfer learning improves forecasting accuracy.

Proposed methods are effective on stock market data.

Abstract

Although transfer learning is proven to be effective in computer vision and natural language processing applications, it is rarely investigated in forecasting financial time series. Majority of existing works on transfer learning are based on single-source transfer learning due to the availability of open-access large-scale datasets. However, in financial domain, the lengths of individual time series are relatively short and single-source transfer learning models are less effective. Therefore, in this paper, we investigate multi-source deep transfer learning for financial time series. We propose two multi-source transfer learning methods namely Weighted Average Ensemble for Transfer Learning (WAETL) and Tree-structured Parzen Estimator Ensemble Selection (TPEES). The effectiveness of our approach is evaluated on financial time series extracted from stock markets. Experiment results reveal that TPEES outperforms other baseline methods on majority of multi-source transfer tasks.