SALES PREDICTION BASED ON SARIMAX TIME SERIES ALGORITHM

Abstract

Businesses must perform time-series forecasting of seasonal item sales because it enables them to predict future demand and modify their inventory accordingly. This investigation compares the performance of three well-known machine learning methods for time-series forecasting of seasonal item sales: Support Vector Machine (SVM), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), and Multi-layer Perception (MLP). A dataset of historical sales data is used to evaluate the algorithms. The data is split into training and testing sets, and measures like Mean Absolute Error (MAE), Relative Absolute Error (RAE), and Root Mean Squared Error (RMSE) are used to assess each algorithm's performance. The analysis's findings support the assertion that SARIMA- X provides greater accuracy than other methods incalculating the seasonal sales of the historical data. Keywords—, Time-series forecasting, SARIMAX, ARIMA, Machine Learning