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| Comparison of the effectiveness of five time series models for prediction ofpulmonary tuberculosis incidence |
| WANG Yingdan1, GAO Chunjie1, WANG Lei2
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1. School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830011, China;
2. Xinjiang Medical University, Urumqi, Xinjiang 830011, China |
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Abstract Objective To compare the effectiveness of seasonal autoregressive integrated moving average (SARIMA) model, additive Holt-Winters model, Holt-Winters' multiplicative model, GM (1, 1) model and linear combination prediction model for prediction of pulmonary tuberculosis incidence. Methods Data pertaining to monthly incidence of pulmonary tuberculosis in Xinjiang Uyghur Autonomous Region from 2004 to 2008 were captured from Public Health Sciences Data Center. The SARIMA model, additive Holt-Winters model, Holt-Winters' multiplicative model, GM (1, 1) model and linear combination prediction model were created based on the incidence of pulmonary tuberculosis from January 2004 to June 2018, to predict the incidence of pulmonary tuberculosis from July to December 2018. The predictive value of each model was evaluated using absolute percentage error (APE), mean APE (MAPE) and root mean square error (RMSE), and the best model was selected based on minimum APE, MAPE and RMSE. Results The SARIMA model showed the minimum APE (10.94%), 11.01% and 7.96% MAPE and 564 and 419 RMSE at the model-fitting and prediction phases; followed by the linear combination prediction model, with 13.71% APE, 12.01% and 7.94% MAPE and 600 and 447 RMSE at the model-fitting and prediction phases, while the additive Holt-Winters model, Holt-Winters' multiplicative model and GM (1, 1) model showed a low predictive value. Conclusion The SARIMA and linear combination prediction models are superior to additive Holt-Winters model, Holt-Winters' multiplicative model and GM (1, 1) model for prediction of pulmonary tuberculosis incidence.
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Received: 06 July 2022
Revised: 21 October 2022
Published: 13 December 2022
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