시간적 계층을 이용한 교통사고 발생건수 예측Temporal hierarchical forecasting with an application to traffic accident counts
- Authors
- Jun, Gwanyoung; Seong, Byeongchan
- Issue Date
- Apr-2018
- Publisher
- KOREAN STATISTICAL SOC
- Keywords
- temporal hierarchies; reconciliation forecast; weighted least square estimator; ARIMA model; exponential smoothing method
- Citation
- KOREAN JOURNAL OF APPLIED STATISTICS, v.31, no.2, pp 229 - 239
- Pages
- 11
- Journal Title
- KOREAN JOURNAL OF APPLIED STATISTICS
- Volume
- 31
- Number
- 2
- Start Page
- 229
- End Page
- 239
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/2307
- DOI
- 10.5351/KJAS.2018.31.2.229
- ISSN
- 1225-066X
2383-5818
- Abstract
- 본 논문에서는 시간적 계층 개념을 활용하여 시계열 자료를 예측하는 방법을 소개한다. 횡단적 계층 자료 분석에서와 유사한 방법으로 중복되지 않는 시간적 계층을 시계열 자료에 구조화할 수 있다. 이러한 시간적 계층을 활용하여 조정된 예측은 기존의 계층별 독립적 기저 예측 및 상향식 예측보다 더 정확하고 강건한 예측값을 생성한다. 실증 분석으로서 국내 교통사고 발생건수를 시간적 계층 개념을 활용하여 예측한다. 분석 결과, 조정 예측이 기존의 다른 예측보다 예측 성능면에서 더 우수함을 확인할 수 있다.
This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.
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