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DOI 10.11817/j.issn.1672-7347.2014.11.011

Медицинская библиотека · Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2014

[Model of multiple seasonal autoregressive integrated moving average model and its application in prediction of the hand-foot-mouth disease incidence in Changsha].

Tan Ting, Chen Lizhang, Liu Fuqiang

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

OBJECTIVE

To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence.

METHODS

EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot- mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand- foot-mouth disease incidence from September 2013 to February 2014 were served as the examined samples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model.

RESULTS

After the data sequence was handled by smooth sequence, model identification and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model fitting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83.

CONCLUSION

The multiple seasonal ARIMA is a good prediction model, and the fitting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.

Для цитирования: Tan Ting, Chen Lizhang, Liu Fuqiang. [Model of multiple seasonal autoregressive integrated moving average model and its application in prediction of the hand-foot-mouth disease incidence in Changsha].. Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences. 2014. DOI: 10.11817/j.issn.1672-7347.2014.11.011.

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