The CTLNet for Shanghai Composite Index Prediction

TL;DR

This paper introduces CTLNet, a hybrid deep learning model combining CNN, Transformer, and LSTM, to improve Shanghai Composite Index prediction, outperforming existing models in accuracy.

Contribution

The paper proposes a novel CNN-Transformer-LSTM hybrid model specifically designed for stock index prediction, demonstrating superior performance over current state-of-the-art methods.

Findings

CTLNet achieves higher prediction accuracy than baseline models.

The hybrid model effectively captures long-term dependencies in time series data.

Experimental results validate the model's superiority in Shanghai Index forecasting.

Abstract

Shanghai Composite Index prediction has become a hot issue for many investors and academic researchers. Deep learning models are widely applied in multivariate time series forecasting, including recurrent neural networks (RNN), convolutional neural networks (CNN), and transformers. Specifically, the Transformer encoder, with its unique attention mechanism and parallel processing capabilities, has become an important tool in time series prediction, and has an advantage in dealing with long sequence dependencies and multivariate data correlations. Drawing on the strengths of various models, we propose the CNN-Transformer-LSTM Networks (CTLNet). This paper explores the application of CTLNet for Shanghai Composite Index prediction and the comparative experiments show that the proposed model outperforms state-of-the-art baselines.