Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model

Yin Ping Chen; Ai Ping Wu; Cui Ling Wang; Hai Ying Zhou; Shu Xiu Feng

doi:10.4028/www.scientific.net/AMR.709.819

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 709Time Series Analysis of Pulmonary Tuberculosis...

Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model

Abstract:

The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)₁₂ was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and practical to apply the approach of ARIMA model in fitting time series to predict pulmonary tuberculosis within a short lead time.

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Periodical:

Advanced Materials Research (Volume 709)

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819-822

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.709.819

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Online since:

June 2013

Authors:

Yin Ping Chen, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, Shu Xiu Feng

Keywords:

ARIMA Model, Forecast, Pulmonary Tuberculosis, Time Series

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