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• W2889348110 startingPage "2833" @default.
• W2889348110 abstract "This study focuses on predicting stock closing prices by using recurrent neural networks (RNNs). A long short-term memory (LSTM) model, a type of RNN coupled with stock basic trading data and technical indicators, is introduced as a novel method to predict the closing price of the stock market. We realize dimension reduction for the technical indicators by conducting principal component analysis (PCA). To train the model, some optimization strategies are followed, including adaptive moment estimation (Adam) and Glorot uniform initialization. Case studies are conducted on Standard & Poor's 500, NASDAQ, and Apple (AAPL). Plenty of comparison experiments are performed using a series of evaluation criteria to evaluate this model. Accurate prediction of stock market is considered an extremely challenging task because of the noisy environment and high volatility associated with the external factors. We hope the methodology we propose advances the research for analyzing and predicting stock time series. As the results of experiments suggest, the proposed model achieves a good level of fitness." @default.
• W2889348110 created "2018-09-07" @default.
• W2889348110 creator A5021099152 @default.
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• W2889348110 date "2018-10-01" @default.
• W2889348110 modified "2023-10-06" @default.
• W2889348110 title "Improving Stock Closing Price Prediction Using Recurrent Neural Network and Technical Indicators" @default.
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• W2889348110 doi "https://doi.org/10.1162/neco_a_01124" @default.
• W2889348110 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30148707" @default.
• W2889348110 hasPublicationYear "2018" @default.
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