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Author: Agarwal, Shweta × End date: 2021 × Start date: 2020 × Type: Thesis × Thesis Type: M.Tech. ×
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    ThesisItemOpen Access
    Stock price prediction using LSTM approach
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Agarwal, Shweta; Singh, B.K.
    In today's economy, the stock market, often known as the equity market, has a significant impact. The rise or decline in the share price has a significant impact on the investor's profit. The proposed method used Long short-Term Memory (LSTM) Approach. Here I am considering multi-column LSTM model which takes more than one column to analyse and train the model and based on that it will predict the values for future days. More than one features helps the model to predict the values more accurately than providing the single feature. Here the dataset is taken from Yahoo Finance website which provides historical data to almost all of the companies listed in the stock market. The dataset is taken for a particular company PETRONET LNG from 2004 to 2018. Next 30 days values are being predicted based on that historical data. The values for 2019 is not being considered as this time was affected by corona virus and every sector of the industry was affected by this pandemic. So taking these values may provide wrong predictions as there was sudden fall and rise in the stock values during this time. I have also added 2 more features to the given historical data i.e. volatility and momentum. Volatility is basically used to capture fluctuation in the market. Momentum tells us what is the changes in the price as compare to past days. Result showa that adding these features helps model to predict more accurately.