In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. But the use of different methods of deep learning has become a vital source of prediction. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. ![]() The stock market serves as an indicator for forecasting the growth of the economy. Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance.
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