Research on the Application of Auto Spare Parts Sales Forecast in the Age of Big Data

It can be seen from the big data platform that the number of private cars increases in geometric form every year. How to effectively predict the sales volume of auto spare parts through the data of the big data platform and achieve reasonable storage management of spare parts is the main content of this research. The paper first preprocesses the original data, and then forecasts the results of auto spare parts sales by using time series analysis and regression decision tree analysis. The experimental results show that the time series analysis method has a good degree of broken line fitting between the predicted sales volume of auto spare parts and the actual sales volume, and the average prediction accuracy is about 90.21%, which can be used to predict the annual sales volume of actual auto spare parts. Therefore, time series analysis is an effective regression prediction method.