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.

Predictive analytics for inventory optimization

“…Among the numerous forecasting methods, ARIMA is a basic time series forecasting technique yet simpler to implement and well-suitable for analyses of data that have trends and/or seasonal patterns. (Wang & Aviles, 2023;Bansal, 2020). Based on many studies, ARIMA consistently underscores as a successful application in various industries, including food and financial markets where time series analysis is presented by emphasizing its ability to adaptability and providing accurate sales forecasts (Wang & Aviles, 2023;Ariyo et al, 2014;Thomassey, 2010).…”

“…(Wang & Aviles, 2023;Bansal, 2020). Based on many studies, ARIMA consistently underscores as a successful application in various industries, including food and financial markets where time series analysis is presented by emphasizing its ability to adaptability and providing accurate sales forecasts (Wang & Aviles, 2023;Ariyo et al, 2014;Thomassey, 2010). According to Ariyo et al, (2014), the robustness and efficiency of ARIMA, especially in short-term prediction is a known fact and has been proven by using the model for financial time series forecasting to predict stock prices.…”

“…The studies state that ARIMA could reasonably compete with any emerging forecasting method, which proves that ARIMA is one of the better approaches for shortterm prediction in sales forecasting as well. However, ARIMA also has some limitations when using the model in the clothing industry, it requires a dataset that covers characteristics of product sales and other business environments (Wang & Aviles, 2023) since fashion trends, seasonality, and consumer preferences play a vital role in the apparel industry.…”