TY - GEN
T1 - Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects
AU - Suhartono,
AU - Lee, Muhammad Hisyam
AU - Prastyo, Dedy Dwi
N1 - Publisher Copyright:
© 2015 AIP Publishing LLC.
PY - 2015/12/11
Y1 - 2015/12/11
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84984532187&partnerID=8YFLogxK
U2 - 10.1063/1.4937108
DO - 10.1063/1.4937108
M3 - Conference contribution
AN - SCOPUS:84984532187
T3 - AIP Conference Proceedings
BT - Innovation and Analytics Conference and Exhibition, IACE 2015
A2 - Ahmad, Nazihah
A2 - Zulkepli, Jafri
A2 - Ibrahim, Adyda
A2 - Aziz, Nazrina
A2 - Abdul-Rahman, Syariza
PB - American Institute of Physics Inc.
T2 - 2nd Innovation and Analytics Conference and Exhibition, IACE 2015
Y2 - 29 September 2015 through 1 October 2015
ER -