# The CTLNet for Shanghai Composite Index Prediction Source: [The CTLNet for Shanghai Composite Index Prediction]() Collection index: [llms.txt]() Data updated: 2026-04-22T05:24:29.470Z Authors: Haibin Jiao Published: 2026-04-18T04:55:36.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Time series forecasting on SSE Composite Index (test)](): Time Series Forecasting; SSE Composite Index (test); MAE: 0.0108. - [Time Series Forecasting on SSE Composite Index50 (test)](): Time Series Forecasting; SSE Composite Index50 (test); MAE: 0.0083. ## Original sources and implementation [Original paper]() [PDF]()