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Time series forecasting based on wavelet filtering 원문보기

  • 저자

    주태우

  • 학위수여기관

    Graduate School, Korea University

  • 학위구분

    국내석사

  • 학과

    産業經營工學科

  • 지도교수

    金性範

  • 발행년도

    2014

  • 총페이지

    v, 30장

  • 키워드

    ARIMA forecasting time series wavelet transforms;

  • 언어

    eng

  • 원문 URL

    http://www.riss.kr/link?id=T13541947&outLink=K  

  • 초록

    Forecasting time series data is one of the most important issues involved in numerous applications in real life. Time series data have been analyzed in either the time or frequency domains. The objective of this study is to propose a forecasting method based on wavelet filtering. The proposed method decomposes the original time series into the trend and variation parts and constructs a separate model for each part. Simulation and real case studies were conducted to examine the properties of the proposed method under various scenarios and compare its performance with time series forecasting models without wavelet filtering. The results from both simulated and real data showed that the proposed method based on wavelet filtering yielded more accurate results than the models without wavelet filtering in terms of mean absolute percentage error criterion.


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