Retail Sales Forecasting Using ARIMA Based on Point-of-Sale Transaction Data

Abstract

Retail businesses generate large volumes of Point-of-Sale (POS) transaction data that can be utilized for sales forecasting and inventory planning. However, transaction-level data need to be transformed into an appropriate time-series structure before forecasting can be performed. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) method to historical POS transaction data for retail sales forecasting. The study used 4,443,905 transaction records from a retail store covering the period from 2018 to 2021. The research process adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM), with the analysis focusing on data preparation, ARIMA modeling, and model evaluation. Sales data were aggregated into product-level time series, and the ARIMA model was identified using stationarity analysis, Autocorrelation Function (ACF), and Partial Autocorrelation Function (PACF). The resulting ARIMA(1,1,1) model was applied to five selected products represented by M01–M05. Model evaluation used the Ljung–Box test and Root Mean Square Error (RMSE), with forecasting conducted for January and February 2022. The results show that forecasting errors varied across products, indicating differences in forecasting performance according to their historical sales patterns. The study demonstrates the applicability of ARIMA for product-level retail sales forecasting based on historical POS transaction data.