Genetic Algorithms for Holt Winter Exponential Smoothing Parameter Optimization in Indonesian Car Sales Forecasting
- DOI
- 10.2991/978-94-6463-106-7_15How to use a DOI?
- Keywords
- Forecasting; Hybrid Method; Genetic Algorithm; Holt-Winter Exponential Smoothing; Regression
- Abstract
The Indonesian automotive industry contributed significantly to the economy. Indonesia has 22 automotive companies that have been operating and have helped absorb many employees. Sales forecasting is considered as future market demand. So that accurate sales forecasting can be used as one of the decision supports for production planning. This study proposes an integration of a genetic algorithm with Holt-Winters exponential smoothing (GA-HW) in Indonesian Car Sales Forecasting. The proposed method can provide Highly Accurate forecasting results for the Toyota, Daihatsu, and Suzuki brands using both GA-MHW and GA-AHW models. Meanwhile, the proposed method on Honda brand provides good forecasting results. MAPE Comparison between the pro-posed method and golden section – HW gave the conclusion that the proposed method outperformed the golden section – HW.
- Copyright
- © 2022 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Mamluatul Hani’ah AU - Ika Kusumaning Putri AU - Ariadi Retno Tri Hayati Ririd PY - 2022 DA - 2022/12/29 TI - Genetic Algorithms for Holt Winter Exponential Smoothing Parameter Optimization in Indonesian Car Sales Forecasting BT - Proceedings of the 2022 Annual Technology, Applied Science and Engineering Conference (ATASEC 2022) PB - Atlantis Press SP - 159 EP - 171 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-106-7_15 DO - 10.2991/978-94-6463-106-7_15 ID - Hani’ah2022 ER -