Demand forecasting by the neural network with discrete Fourier transform

被引：2
|
作者：
Yohda, M
Saito-Arita, M
Okada, A
Suzuki, R
Kakemoto, Y
机构：
来源：
2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2002年
关键词：
D O I：
10.1109/ICDM.2002.1184052
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes a new demand forecasting method using the Neural Network and Fourier Transform. In this method, time series data of sales results considered as a combination of frequency are transformed into several frequency data. They are identified from objective indexes that consist of product properties or economic indicators and so forth. This method is efficient for demand forecasting aimed at new products that have no historical data.
引用
收藏
页码：779 / 782
页数：4
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