Abstract: Because the stock market is a very complicated nonlinear movement system whose fluctuation law is influenced by a wide range of factors, forecasting the stock price index is challenging. Numerous examples show how neural network algorithms can accurately forecast time series and frequently produce results that are adequate. The two stocks' short-term closing values were predicted using a Regularised GRULSTM neural network model that we created in this paper based on existing models. Experiments show that our suggested model predicts stock time series better than the existing GRU and LSTM network models.