SARIMA with Sliding Window Implementation for Forecasting Seasonal Demand Data
DOI:
https://doi.org/10.23887/janapati.v13i1.59971Keywords:
Forecasting, SARIMA, Decomposition, Windowing, Seasonal, DemandAbstract
Demand forecasting is an essential part of business process management. A comparison of methods is needed to get the best model to provide good forecasting results. Difficulties in meeting consumer demands and predicting these requests using demand data at companies CV. ABCD is the main problem in this research. The SARIMA and decomposition methods are used for comparison and search for the best model before forecasting. SARIMA with a windowing size of 56, indicating the smallest MAPE value of 3,91%. The value <10%, so it can be said to produce an excellent forecasting value. Forecasting results with SARIMA show a meeting between actual and forecasting data in 2022. Therefore, it can be said the forecasting results for 2023 and 2024 can be used as a reference for the company CV. ABCD to meet customer demand and avoid stock shortages.
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