응용통계연구 (The Korean Journal of Applied Statistics)

  • 제32권6호
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  • Pages.851-865
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  • 2019
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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다중 결합 예측 알고리즘을 이용한 교통사고 발생건수 예측

Multiple aggregation prediction algorithm applied to traffic accident counts

  • 배두람 (중앙대학교 응용통계학과) ;
  • 성병찬 (중앙대학교 응용통계학과)
  • Bae, Doorham (Department of Applied Statistics, Chung-Ang University) ;
  • Seong, Byeongchan (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2019.08.06
  • 심사 : 2019.10.04
  • 발행 : 2019.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

하나의 시계열 자료에서 다양한 특징을 발견하는 일은 간단한 문제가 아니다. 본 논문에서는 하나의 시계열 자료에서 복수의 패턴을 찾아내어 예측 정확도를 높이는 방식인 다중 결합 예측 알고리즘을 소개한다. 이 알고리즘은 시간적 결합과 예측값 조합의 개념을 사용한다. 시간적 결합 방식을 통해, 하나의 시계열 자료에서 여러 개의 시계열 자료를 생성할 수 있으며, 각각의 자료는 별도의 특성을 가지게 된다. 여러 개의 시계열 자료에서 다양한 특성을 추출하기 위하여 지수평활법을 사용하고 시계열 요소들 및 이들의 예측값을 계산한다. 마지막 단계에서 시계열 요소 별로 예측값을 혼합 한 후, 각 시계열 요소들의 조합값을 더하여 최종 예측값을 만든다. 실증 분석으로 국내 교통사고 발생 건수를 예측한다. 분석 결과, 기존의 다른 예측 방식보다 예측 성능이 우수함을 확인할 수 있다.

Discovering various features from one time series is complicated. In this paper, we introduce a multi aggregation prediction algorithm (MAPA) that uses the concepts of temporal aggregation and combining forecasts to find multiple patterns from one time series and increase forecasting accuracy. Temporal aggregation produces multiple time series and each series has separate properties. We use exponential smoothing methods in the next step to extract various features of time series components in order to forecast time series components for each series. In the final step, we blend predictions of the same kind of components and forecast the target series by the summation of blended predictions. As an empirical example, we forecast traffic accident counts using MAPA and observe that MAPA performance is superior to conventional methods.

키워드

참고문헌

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