Explore the Impact of Initial Data Coherence on ARIMA Model Prediction Based on Python

Abstract

As the most intuitive economic indicator reflecting the economic development level of a company, an industry, or even a country, the fluctuation of stock prices will attract great attention. In order to avoid the loss of investors to a certain extent, stock price prediction is widely used in the stock market to prevent risks to a certain extent, and the ARIMA model is a time series prediction model that has been widely used in recent years. Therefore, the application of the ARIMA model to stock price forecasting is the main research object of this paper. This paper speculated at the beginning of the study that there may be a close relationship between the degree of fit of the ARIMA model and the consistency of the prediction results with the original data used. However, the existing literature is basically blank in this aspect of research and analysis. Therefore, this paper chooses to conduct comparative experiments on a set of continuous data and a set of discontinuous data to analyze the influence of the consistency of the original data under application conditions on the ARIMA model fitting and prediction results, hoping to fill the gaps in the existing literature through this research. The experimental results show that in some cases of original data with obvious discontinuous data missing, its smoothness is extremely poor, resulting in a poor fitting effect of the ARIMA model, and it can not be applied to the prediction of such data.