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http://dx.doi.org/10.5351/KJAS.2019.32.6.851

Multiple aggregation prediction algorithm applied to traffic accident counts  

Bae, Doorham (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.32, no.6, 2019 , pp. 851-865 More about this Journal
Abstract
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.
Keywords
temporal aggregation; combination; multiple aggregation prediction algorithm; time series components; exponential smoothing method;
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