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http://dx.doi.org/10.5351/KJAS.2018.31.2.229

Temporal hierarchical forecasting with an application to traffic accident counts  

Jun, Gwanyoung (Department of Applied Statistics, Chung-Ang University)
Seong, Byeongchan (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.2, 2018 , pp. 229-239 More about this Journal
Abstract
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.
Keywords
temporal hierarchies; reconciliation forecast; weighted least square estimator; ARIMA model; exponential smoothing method;
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Times Cited By KSCI : 1  (Citation Analysis)
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