融合CNN、Transformer与LSTM，提升上证指数预测精度。
The CTLNet for Shanghai Composite Index Prediction
- 结合CNN、Transformer与LSTM结构，捕捉多维度时序特征。
- 在上证指数预测中优于现有主流模型，表现更优。
- 适合金融时间序列预测研究者及量化投资者参考。
上证综合指数预测已成为众多投资者和学术研究者关注的热点。深度学习模型广泛应用于多变量时间序列预测，包括循环神经网络（RNN）、卷积神经网络（CNN）和变换器（Transformer）。其中，基于注意力机制和并行处理能力的Transformer编码器，在处理长序列依赖和多变量相关性方面具有优势。借鉴各类模型优点，本文提出一种CNN-Transformer-LSTM网络（CTLNet），探索其在上证综合指数预测中的应用。对比实验表明，所提模型优于当前先进基准方法。
原文摘要 · Abstract (English)
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.
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