用于股票预测的基于注意力的 CNN-LSTM 和 XGBoost 混合模型,arXiv - CS - Machine Learning

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用于股票预测的基于注意力的 CNN-LSTM 和 XGBoost 混合模型
arXiv - CS - Machine Learning Pub Date : 2022-04-06 , DOI: arxiv-2204.02623
Zhuangwei Shi, Yang Hu, Guangliang Mo, Jian Wu

股票市场在经济发展中发挥着重要作用。由于股票市场的复杂波动性，对股票价格变化的研究和预测，可以为投资者规避风险。传统的时间序列模型ARIMA不能描述非线性，在股票预测中也不能取得满意的结果。由于神经网络具有很强的非线性泛化能力，本文提出了一种基于注意力的 CNN-LSTM 和 XGBoost 混合模型来预测股票价格。本文构建的模型将时间序列模型、带注意机制的卷积神经网络、长短期记忆网络和XGBoost回归器以非线性关系集成，提高了预测精度。该模型可以充分挖掘股票市场多个时期的历史信息。股票数据首先通过 ARIMA 进行预处理。然后，采用在预训练-微调框架中形成的深度学习架构。预训练模型是基于序列到序列框架的基于注意力的CNN-LSTM模型。该模型首先使用卷积提取原始股票数据的深层特征，然后使用 Long Short-Term Memory 网络挖掘长期时间序列特征。最后采用 XGBoost 模型进行微调。结果表明，混合模型更为有效，预测准确率较高，可以帮助投资者或机构进行决策，达到扩大收益、规避风险的目的。源代码可在 https://github 获得。

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Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Stock market plays an important role in the economic development. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. The traditional time series model ARIMA can not describe the nonlinearity, and can not achieve satisfactory results in the stock prediction. As neural networks are with strong nonlinear generalization ability, this paper proposes an attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price. The model constructed in this paper integrates the time series model, the Convolutional Neural Networks with Attention mechanism, the Long Short-Term Memory network, and XGBoost regressor in a non-linear relationship, and improves the prediction accuracy. The model can fully mine the historical information of the stock market in multiple periods. The stock data is first preprocessed through ARIMA. Then, the deep learning architecture formed in pretraining-finetuning framework is adopted. The pre-training model is the Attention-based CNN-LSTM model based on sequence-to-sequence framework. The model first uses convolution to extract the deep features of the original stock data, and then uses the Long Short-Term Memory networks to mine the long-term time series features. Finally, the XGBoost model is adopted for fine-tuning. The results show that the hybrid model is more effective and the prediction accuracy is relatively high, which can help investors or institutions to make decisions and achieve the purpose of expanding return and avoiding risk. Source code is available at https://github.com/zshicode/Attention-CLX-stock-prediction.

更新日期：2022-04-06

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