Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

• DOI: 10.1063/1.4937108
• Publication Date: 2015-12-12T18:00:51Z
• Authors: Suhartono; Muhammad Hisyam Lee; Dedy Dwi Prastyo
• Keywords: 0103 physical sciences; 01 natural sciences

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men’s jeans and women’s trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.