Abstract
The intricate realm of time series prediction using stock market datasets from the NSE India is delved into by this research. The supremacy of LSTM architecture for forecasting in time series is initially affirmed, only for a paradigm shift to be encountered when exploring various LSTM variants across distinct sectors on the NSE (National Stock Exchange) of India. Prices of various stocks in five different sectors have been predicted using multiple LSTM model variants. Contrary to the assumption that a specific variant would excel in a particular sector, the Gated Recurrent Unit (GRU) emerged as the top performer, prompting a closer examination of its limitations and subsequent enhancement using technical indicators. The ultimate objective is to unveil the most effective model for predicting stock prices in the dynamic landscape of NSE India.
Published Version
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