Financial Time Series Forecasting using CNN and Transformer

TL;DR

This paper introduces a hybrid CNN-Transformer model for financial time series forecasting, effectively capturing both short-term and long-term dependencies to predict stock price movements.

Contribution

It proposes a novel combination of CNNs and Transformers to improve the modeling of dependencies in financial time series data.

Findings

Outperforms traditional statistical methods.

Achieves higher accuracy in stock movement prediction.

Demonstrates effectiveness on S&P 500 data.

Abstract

Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term temporal dependencies between data points. Convolutional Neural Networks (CNN) are good at capturing local patterns for modeling short-term dependencies. However, CNNs cannot learn long-term dependencies due to the limited receptive field. Transformers on the other hand are capable of learning global context and long-term dependencies. In this paper, we propose to harness the power of CNNs and Transformers to model both short-term and long-term dependencies within a time series, and forecast if the price would go up, down or remain the same (flat) in the future. In our experiments, we demonstrated the success of the proposed method in comparison to commonly adopted statistical and deep learning methods on forecasting intraday stock price change of S&P 500 constituents.