The Stock Price Forecasting Based on Time Series Model and Neural Network

Abstract

The ARIMA model and LSTM model are commonly seen in stock price forecasting, and their excellent performance in processing and forecasting time series data has made them widely used and popular. They are used to predict future values with relatively high accuracy and small error values. Based on the excellent performance of LSTM model and ARIMA model on time series, this paper discusses the use and findings of ARIMA model and LSTM model in empirical studies and indicates whether they can be used for stock forecasting. This paper highlights the advantages and disadvantages of ARIMA model and LSTM model by comparing and summarizing the different applications. This paper finally finds that ARIMA model and LSTM model have high accuracy in predicting stock market. In this paper, the performance results of the ARIMA model and the LSTM model are extensively illustrated by using different approaches to the use of these two models. It also shows the accuracy of ARIMA model and LSTM model.