Enhancing Stock Price Prediction Method Based on CNN-LSTM hybrid model

Yuqi Ding

doi:10.54097/hbem.v21i.14760

Authors

Yuqi Ding

DOI:

https://doi.org/10.54097/hbem.v21i.14760

Keywords:

Convolutional Neural Network, Long Short Term Memory, Stock Price Prediction.

Abstract

Stock price prediction means using the previous data to predict future trend. With the development of deep learning technologies in artificial intelligence area, many works try to use single CNN or single LSTM to make prediction. However, both these two models have their own weakness and strengths. In this paper, we try to make a CNN-LSTM hybrid model to achieve a better performance on stock price prediction. It proposes that the CNN-LSTM hybrid model predicts stock prices using RMSE to measure higher accuracy than a single CNN and a single LSTM model. The experiment shows a great ability on generalization and provide reliable results, which can apply to other stocks to make prediction and give suggestions on investing strategy. The CNN-LSTM model can play the respective advantages of CNN and LSTM model to improve the accuracy of stock price prediction and has better performance in both short-term prediction and long-term trend.

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