Earticle

초록

영어

As the economy grows, interest in financial investment is increasing. One of the most representative ways of investing in finance is stocks. When investing stocks, the stock price index, which indicates the situation in the market, has a lot of influence on investors' decisions toward effective investment. Until now, traditional time series analysis methods and machine learning methods have been used to perform analyses that predict stock price index data. In this study, a model for analyzing and predicting stock price index data using functional linear methods was presented. By using comparison with functional linear model to present and predict the DTW clustering method implemented using the same data, the efficiency of functional linear model was explained. In the full-scale analysis, the stock price index data were expressed by converting it into functional data through smoothing using linear combinations of base functions, and the functional linear model was implemented and predicted. For comparison, the model was implemented and predicted through DTW clustering. Use DTW methods to classify patterns in data and to identify and predict patterns most similar to those in the forecast period. As a result, you want to compare the accuracy of the two models with MAPE, RMSE, and see the possibility of a functional linear model.

목차

Ⅰ. 서론
Ⅱ. 함수적 자료 분석 방법
Ⅲ. 실제 자료에 적용 및 예측
Ⅳ. 결론
참고문헌
Abstract

키워드

저자

• 이미영 [ Miyoung Lee | 건국대학교 데이터사이언스학과 교수 ] 주저자
• 권성재 [ Sung-Jae Kwon | 건국대학교 데이터사이언스학과 석사 ] 교신저자

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  생산성연구: 국제융합학술지 [Productivity Research: An International Interdisciplinary Journal]

• 간기

  격월간

• pISSN

  1225-3553

• 수록기간

  1987~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 325 DDC 330