Comparative analysis of ARIMA model and neural network in predicting stock price

Abstract

ARIMA model is a time series model, which is used to analyze and predict the mean value of the sequence. Neural network can be used to predict in various fields. In this essay, the neural network algorithm is used for the financial time series to predict the trend of stock price change, and the results are compared with the traditional ARIMA. It is found that the neural network algorithm can better predict the change of stock price. In the empirical analysis, Python is used to establish three models for analysis and prediction, which can provide a more appropriate model reference for investors to judge the short-term trend of stock prices and portfolio decision-making.