ARIMA vs LSTM Algorithm – A Comparative Study Based on Stock Market Prediction

Machine learning, in contrast to traditional algorithms and models, is a systematic and complete application of computer algorithms and statistical models that has been widely applied in a variety of industries. Machine learning is primarily utilized in finance to examine the future trend of capital market prices. In this research, we used traditional models and machine learning models for forecasting the linear and non-linear problems, respectively, to predict stock time-series data. To begin, stock samples from the National Stock Exchange from 2010 to 2019 are collected. To train and predict stock price and stock price sub correlation, the ARIMA (autoregressive integrated moving average model) and LSTM (long short-term memory) neural network models are used.