자동 특징 추출기법에 의한 최소의 주식예측 특징선택

국문 초록 (Abstract)

본 논문은 가중 퍼지소속함수 기반 신경망(Neural Network with Weighted Fuzzy Membership Functions, NEWFM)기반의 자동 특징 추출기법을 사용하여 1일 후의 주식 예측을 하는 방안을 제안하고 있다. 비중복면적 분산측정법에 의해 중요도가 가장 낮은 특징입력을 자동적으로 하나씩 제거하면서 최소의 특징입력을 선택하였다. 특징입력으로써 CPPn,m (Current Price Position of the day n)과 최근 32일간의 CPPn,m을 웨이블릿 변환한 38개의 계수들 중 비중복면적 분산측정법에 의해서 자동적으로 추출된 2개의 계수가 사용되었다. 제안된 방법으로 1989년부터 1998년까지의 실험군을 사용한 결과로써 60.93%의 예측율을 나타내었다.

다국어 초록 (Multilingual Abstract)

This paper presents a methodology to 1-day-forecast stock index using the automatic feature extraction method based on the neural network with weighted fuzzy membership functions (NEWFM). The distributed non-overlap area measurement method selects the minimized number of input features by automatically removing the worst input features one by one. CPPn,m (Current Price Position of the day n: percentage of the difference between the price of the day n and the moving average from the day n-1 to the day n-m) and the 2 wavelet transformed coefficients from the recent 32 days of CPPn,m are selected as minimized features using bounded sum of weighted fuzzy membership functions (BSWFMs). For the data sets, from 1989 to 1998, the proposed method shows that the forecast rate is 60.93%.

목차 (Table of Contents)

• 요약
• Abstract
• 1. 서론
• 2. KOSPI 예측 모델의 개요
• 3. 특징 선택(비중복적 분산 측정법)
• 4. 실험 결과(Experimental Results)
• 5. 결론
• 참고문헌
• 저자소개

참고문헌 (Reference)

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