Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.

ABSTRACT:

Background

Accurate forecasting model for under-five mortality rate (U5MR) is essential for policy actions and planning. While studies have used traditional time series modeling techniques (e.g., autoregressive integrated moving average (ARIMA) and Holt-Winters smoothing exponential methods), their appropriateness to predict noisy and non-linear data (such as childhood mortality) has been debated. The objective of this study was to model long-term U5MR with group method of data handling (GMDH)-type artificial neural network (ANN), and compare the forecasts with the commonly used conventional statistical methods-ARIMA regression and Holt-Winters exponential smoothing models.

Methods

The historical dataset of annual U5MR in Nigeria from 1964 to 2017 was obtained from the offic

SUBMITTER: Adeyinka DA 

PROVIDER: S-EPMC7712624 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature