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.
翻译:股市在经济发展中起着重要作用。 由于股票市场波动复杂,对股票价格变化的研究和预测可以避免投资者的风险。 传统的时间序列模型ARIMA无法描述非线性,也无法在股票预测中取得令人满意的结果。 由于神经网络具有很强的非线性概括能力,本文件建议采用基于关注的CNN-LSTM和XGBoost混合模型来预测股票价格。 本文构建的模型整合了时间序列模型、 带有注意机制的Convolutional神经网络、 长短期内存网络和 XGBoost在非线性关系中的递增器以及提高预测准确性。 该模型可以在多个时期充分挖掘股票市场的历史信息。 股票数据首先通过ARIMA进行预处理。 然后,采用在培训前调整框架中形成的深层学习架构。 培训前模型是基于注意的CNN-LSTM的源源代码模型, 以注意机制、 长期内存网络和后期退框架为后退框架。 模型首先利用短期内存数据模型, 将MIS- 的原始数据库 用于长期流流数据流数据流到后流数据流 。