Earticle

초록

영어

The purpose of this study is to support the decision of semiconductor companies by providing an objective chip price prediction model. Existing statistical or econometric models have shown limits analyzing nonlinear time-series data such as share prices and exchange rates. The back-propagation algorithm, which is the most common method, was used as the learning algorithm. Predicting was attempted by using two supply factor variables and four demand factor variables. The data used in the analysis was collected from January 3, 2003 to December 28, 2005. The data has been divided into two parts for learning and verification. As a result of inputting the verification data into the trained neural network, the actual values show some differences. However, we were able to see that the flow of the semiconductor market and short-term forecasting was possible providing very little error between the predicted value and the actual value.

목차

Abstract
 1. Introduction
 2. Literature review and Back-propagation neural network
 3. The semiconductor prices prediction using a neural network
  3.1 Variable selection
  3.2 Data normalization
  3.3 The construction of neural network model for the semiconductor pricesprediction
  3.4 The neural network model prediction and evaluation
 4. Conclusions
 References

키워드

저자

• Jongchang Ahn [ Professor at Hanyang University ]
• Ook Lee [ Professor at Hanyang University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658