The CTLNet for Shanghai Composite Index Prediction
Incremental improvement for stock index prediction, a domain-specific problem.
The paper proposes CTLNet, a hybrid deep learning model combining CNN, Transformer, and LSTM, for predicting the Shanghai Composite Index. Comparative experiments show it outperforms state-of-the-art baselines.
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.