SSA를 이용한 일 단위 물수요량 단기 예측에 관한 연구

A Study of Short Term Forecasting of Daily Water Demand Using SSA

  • 투고 : 2004.09.13
  • 심사 : 2004.12.01
  • 발행 : 2004.12.15

초록

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

키워드

참고문헌

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