Title: An ARIMA based model for forecasting the patient number of epidemic disease
Abstract:Forecasting the number of epidemic disease is very important for CDC (center for disease control and prevention). To improve the forecast accuracy, an ARIMA (autoregressive integrated moving average) ...Forecasting the number of epidemic disease is very important for CDC (center for disease control and prevention). To improve the forecast accuracy, an ARIMA (autoregressive integrated moving average) based model is proposed in this paper. First, autocorrelation (AC) and partial autocorrelation (PAC) analysis are introduced to establish a stationary time series, where the autocorrelation order, moving average order and difference order are estimated. Secondly, least square s method (LS) is employed to estimate the parameters of the prediction model. Finally, the real data between Jan. and Aug. 2014 coming from a CDC are fed into the proposed model and the forecast accuracy obtained is 92.1%, which significantly outperforms the simple moving average method currently used in the CDC.Read More
Publication Year: 2016
Publication Date: 2016-06-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 21
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