The CTLNet for Shanghai Composite Index Prediction

Computer Science > Artificial Intelligence [Submitted on 18 Apr 2026] The CTLNet for Shanghai Composite Index Prediction Haibin Jiao 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.16835 [cs.AI]   (or arXiv:2604.16835v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.16835 Focus to learn more Submission history From: Haibin Jiao [view email] [v1] Sat, 18 Apr 2026 04:55:36 UTC (1,197 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)