The Application of ARIMA Model in Short-Term Stock Price Prediction

Abstract

The stock market holds a crucial position in the financial system. Stock price prediction is of great significance to both investors and financial institutions. However, due to its complexity and uncertainty, precise prediction is quite challenging. The ARIMA model, as a classic time series analysis method, performs exceptionally well in handling time series data with trends and seasonality. This article focuses on its application in short-term stock price prediction, elaborates on the model principle and modeling steps, analyzes the factors influencing the prediction, and explores strategies to enhance the prediction effect. The results show that the ARIMA model has certain value in short-term prediction, but it needs to be improved and optimized. Finally, summarize the research and look forward to the future direction.