Stock Price Prediction Based on LSTM in Python
DOI:
https://doi.org/10.54691/bcpbm.v40i.4398Keywords:
Stock price prediction; data analysis; LSTM.Abstract
To reduce the risk of people buying and increase the rate of return, applying a suitable model to predict stock prices is necessary. The research applies Long Short Term Memory (LSTM), which is a kind of artificial neural network in machine learning and is also a special kind of RNN. Through studying this model, the memory of LSTM is like human memory, which is composed of short-term memory and long-term accumulation. In this paper, the current data of 2020 and 2021 is to train, and the data from 2017 to 2019 is for testing. The data comes from three companies, GOOGLE, APPLE and AMAZON in the S&P500 index. Meanwhile, Mean Squared Error (MSE) is used for evaluation. Finally, in comparison with the different training times, LSTM presents good applicability, achieving negligible MSE.
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