label='真实值')plt.plot(df_for_testing.index[window_size:,],pred_test,color='blue',label='预测值')plt.title('Stock Price Prediction')plt.xlabel('Time')plt.xticks(rotation=45)plt.ylabel('Stock Price')plt.legend()plt.show()
Li C , Zhang X , Qaosar M , et al. Multi-factor Based Stock Price Prediction Using Hybrid Neural Networks with Attention Mechanism[C]// 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Com...
In this study, CNN extracts features related to stock price prediction, and LSTM reflects the long-term history process of input time series data. Basic stock price data and technical indicator data for the last 20 days prepare a data set to predict the next day's closing price, and then ...
效率更高,模型训练时间更短,且模型的预测效果相近,表明本文构建的BO-CNN-LSTM模型在股价预测的精度与耗时上具有一定的优势.关键词:股价预测,贝叶斯优化算法,神经网络模型,沪深300指数IICNN-LSTMSTOCKPRICEPREDICTIONMODELBASEDONBAYESIANOPTIMIZATIONAbstractWiththedevelopmentofthefinancialmarket,peopleareincreasinglypaying...
The model effect is still at a relatively good level, which verifies the effectiveness and feasibility of theintroduction of MDT technology.Key words:stock price prediction; multi-directional delayed embedding(MDT); convolutional neural network(CNN);long-short-term memory network(LSTM)作者简介:曹超凡(...
modelwhichcombineshistoricalstockdata,newsemotionandinvestorcomment emotion.ThismodelcombinestheadvantagesofCNNneuralnetwork,LSTM neuralnetworkandattentionmechanismtomakemoreaccurateprediction.Firstly, thenewsandinvestorcommentdatawerepreprocessed,Bertwasusedtoencode ...
原文:Stock price prediction using LSTM, RNN and CNN-sliding window model 股票市场或股票市场对当今经济产生深远影响。股价的上涨或者下跌对投资者的收益具有重要的决定作用。现有的预测方法使用线性(AR,MA,ARIMA)和非线性算法(ARCH,GARCH,神经网络),但它们侧重于使用每日结算预测单个公司的股票指数变动或价格预测 ...
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, ...
# Select the last 75 values of the sequence to start the prediction preds = train_norm[-window_size:].tolist() # set to eval mode model.eval() # Each step of the cycle represents sliding back one space in the time series for i in range(future): ...
[9] YANG H,PAN Z,TAO Q.Robust and Adaptive Online Time Series Prediction with Long Short-Term Memory[J].Computational Intelligence and Neuroscience,2017:1-9. [10] LIU S,CHAO Z,MA J:CNN-LSTM Neural Network Model for Quantitative Strategy Analysis in Stock Markets.In International Conference ...