Contrasting Univariate and Multivariate Time Series Forecasting Methods for Sales: A Comparative Analysis

Abstract

In commodity-based industries, accurate sales forecast is very important for effective inventory management and decision-making. Univariate and multivariate time series forecasting methods have been widely used to predict commodity sales. The purpose of this study is to make a comprehensive comparative analysis of these two methods under the background of commodity sales forecast. Firstly, the concept of time series prediction and its significance in the field of commodity sales are introduced. It emphasizes the challenges related to sales forecast, including demand fluctuation, seasonality and external factors affecting sales model. The forecasting methods of univariate time series, such as ARIMA, are discussed in detail, focusing on their ability to capture the time correlation in a single sales variable. In contrast, the multivariate time series prediction method considers the relationship between multiple variables. Vector autoregressive and multivariate extension of ARIMA. These techniques combine the interaction of various factors, including external influences, to improve the accuracy of prediction. In order to make a comprehensive comparative analysis, a data set including historical sales data of specific commodities is used. Both univariate and multivariate models are suitable for forecasting future sales, and their performance indicators are evaluated by MASE. The results show that although univariate models are easier to implement and explain, they often fail to capture the complex interdependence between different factors that affect sales. On the other hand, the multivariate model shows excellent prediction accuracy by integrating related variables and their dynamic relationships. However, they need more additional data and more complex modeling techniques. Finally, the research gives the practical significance and suggestions of choosing an appropriate forecasting method based on the characteristics of commodity sales data and the specific business environment. It emphasizes the importance of considering the accuracy and interpretability of the prediction model in practical application.