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2017•PubMedRequires access

[Application of ARIMA model to predict number of malaria cases in China].

H Hui-Yu, S Hua-Qin, Z Shun-Xian, Andy Lin, Liqiu Yan, C Yu-Chun, LI Shi-zhu, T Xue-Jiao, Y Chun-Li, Honglian Wei, C. Jiaxu

Open publisher page 4 citations

Abstract

The establishment and prediction of ARIMA model is a dynamic process, which needs to be adjusted unceasingly according to the accumulated data, and in addition, the major changes of epidemic characteristics of infectious diseases must be considered.

About this research paper

What this paper is about

The establishment and prediction of ARIMA model is a dynamic process, which needs to be adjusted unceasingly according to the accumulated data, and in addition, the major changes of epidemic characteristics of infectious diseases must be considered.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The establishment and prediction of ARIMA model is a dynamic process, which needs to be adjusted unceasingly according to the accumulated data, and in addition, the major changes of epidemic characteristics of infectious diseases must be considered.

Key concepts: Autoregressive integrated moving average, Malaria, Statistics, Moving average, Time series, China, Econometrics, Computer science

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[Application of ARIMA model to predict number of malaria cases in China]. — Research Paper | ScholarLens