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http://dx.doi.org/10.5351/KJAS.2020.33.2.147

Automatic order selection procedure for count time series models  

Ji, Yunmi (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.33, no.2, 2020 , pp. 147-160 More about this Journal
Abstract
In this paper, we study an algorithm that automatically determines the orders of past observations and conditional mean values that play an important role in count time series models. Based on the orders of the ARIMA model, the algorithm constitutes the order candidates group for time series generalized linear models and selects the final model based on information criterion among the combinations of the order candidates group. To evaluate the proposed algorithm, we perform small simulations and empirical analysis according to underlying models and time series as well as compare forecasting performances with the ARIMA model. The results of the comparison confirm that the time series generalized linear model offers better performance than the ARIMA model for the count time series analysis. In addition, the empirical analysis shows better performance in mid and long term forecasting than the ARIMA model.
Keywords
count time series; automatic algorithm; time series generalized linear model; ARIMA model;
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