Stock price prediction with sca-lstm network and statistical model arima-garch

HIGHLIGHTS

• What: The aim of this study is to predict the closing price of the stock using (LSTM) network modified by_(SCA) Autoregressive Integrated Moving Average (ARIMA) and Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) statistical models which is called LSTM-SCAARIMA-GARCH model. The existing work focuses on historical stock price variations as time series predictions . In this LSTMNP-SMP method, the main objective is to improve the prediction with less errors. According to the model features introduced earlier (section 2.3), ARIMA is 10 used to model the long-term data and GARCH is used to model . . .
  

   

  

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