Application and comparison of residual autoregressive model and Holt’s two-parameter exponential smoothing model in infant mortality prediction in some countries along the Belt and Road Initiative / 中华疾病控制杂志

ABSTRACT

@# Objective To explore the application of residual autoregressive model and Holt's two-parameter exponential model in the prediction of infant mortality rate in some countries along the “Belt and Road” (China-Indo-China Peninsula Economic Corridor). Methods The time series data of infant mortality rate in Vietnam, Laos, Cambodia, Myanmar, Thailand, Singapore, Malaysia, and China for 1978-2013 were used as training set to fit residual autoregressive model and Holt's two-parameter exponential model. The 2014-2016 data was used as the validation set to compare the performance of model prediction. Results The akaike information criterion (AIC) value of the residual autoregressive model was superior to Holt's two-parameter exponential model. Both prediction models showed high accuracy, and most evaluation indicators (absolute error and relative error) of residual autoregressive prediction model were smaller than Holt's two-parameter exponential model. The residual autoregressive models of Laos, Myanmar and Cambodia were better than the Holt’s two-parameter exponential model for the infant mortality rate(IMR) prediction in different years. Conclusions The residual autoregressive model and Holt's two-parameter exponential model performed well in infant mortality rate prediction in some countries along the China-Indo-china Peninsula Economic Corridor. The residual autoregressive model has better fitting effect. The residual autoregressive model for infant mortality prediction is superior to the Holt two-parameter exponential model in most countries in most years.