Sales Prediction Based on ARIMA Time Series and Multifactorial Linear Model

Abstract

Contemporarily, sales forecasting based on historical data is one of the key topics of research. Researchers have found that different analytical forecasting models can predict the future sales volume to a certain extent based on different actual conditions. However, it is also very important to compare the prediction results of these models, and a reasonable choice of models can help managers to make more scientific decisions. Therefore, in this paper, the historical sales data of a store on the Kaggle is utilized to make predictions based on different models, and investigate the impact of linear regression model and time series model on sales prediction. In this paper, multiple factorial linear regression model and ARIMA model in time series analysis will be adopted based on R to build prediction models to forecast sales volume, respectively. According to the analysis, the root mean square error of the prediction results under the multiple factorial linear regression model and the ARIMA model were 21.135 and 23.221, respectively. The study surface that there were differences in the degree of fit of the prediction results by different pairs of models. Therefore, the choice of the researcher needs to select the appropriate model for prediction according to the specific object of analysis, combined with the data characteristics to achieve the expected results. These results shed on guiding further exploration of differences in predictions from different models.