Research on Sales Forecasting Method for Transmission Parts of Customized Production
- DOI
- 10.2991/cimns-18.2018.50How to use a DOI?
- Keywords
- product sales forecasting; ARIMA model; LSTM model; ARIMA-LSTM hybrid model
- Abstract
With the advancement of manufacturing technology and the diversification of customer needs, product sales forecasting is critical to the company in a small-volume, multi-variety production model. Based on the traditional ARIMA model and the deep neural network LSTM model, this paper constructs the ARIMA-LSTM hybrid model to predict the sales of transmission parts manufacturing enterprises. The model uses the LSTM neural network to describe the linear and nonlinear components of the time series rather than the simple additive relationship in the traditional hybrid model. Therefore, the model overcomes the problems of low prediction accuracy that may exist in the traditional hybrid model. This paper selects the historical sales data of a certain manufacturer of a large transmission parts manufacturer in Sichuan, and carries out experimental verification on the constructed ARIMA-LSTM hybrid model. The results show that the model has high prediction accuracy and can provide reference for enterprise decision-making. The research work of this paper is supported by Sichuan Science & Technology Program under Grant No.2017GZ0144 & 2017GZ0047.
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jun Cheng AU - Tao Hong AU - Xiaohong Li AU - Honglin Wang PY - 2018/11 DA - 2018/11 TI - Research on Sales Forecasting Method for Transmission Parts of Customized Production BT - Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018) PB - Atlantis Press SP - 220 EP - 224 SN - 2352-538X UR - https://doi.org/10.2991/cimns-18.2018.50 DO - 10.2991/cimns-18.2018.50 ID - Cheng2018/11 ER -