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An Efficient Model for Stock Price Forecasting using Long Short Term Memory

Sleeba Mathew C, Ahmed Unaish, Mahammad Akbar, Mahammad Ishan, Mahammed Dhansih Raza

Abstract


One of the most challenging tasks in the realm of computation is stock market forecasting. Numerous factors, including physical ones, physiological ones, rational and irrational behaviour, investor attitude, market rumours, etc., have a role in the prediction. These factors all work together to make stock values unpredictable and highly challenging to forecast accurately.

In order to predict future trends, we use Machine Learning (ML) algorithms using past stock price data. To forecast the price of stocks in the future, we employ the LSTM (Long Short Term Memory) model. LSTMs are crucial because they can store previous or past data, which makes them extremely effective in solving problems involving sequence prediction. This is crucial for market prediction since it allows us to accurately estimate future stock values by storing and reading historical stock data. With the help of Dash, a Python framework, and an LSTM recurrent neural network, we have developed a single-page web application that displays company information (logo, registered name, and description) and stock plots based on the stock code (ticker) provided by the user. Additionally, the ML model allows the user to obtain predicted stock prices for the N number of days they have input.


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References


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