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Raicharoen, T., Lursinsap, C. and Sanguanbhoki, P. (2003) Application of Critical Support Vector Machine to Time Series Prediction. Proceedings of the International Symposium on Circuits and Systems, Bangkok, 25-28 May 2003, 741-744.
https://doi.org/10.1109/iscas.2003.1206419

has been cited by the following article:

• TITLE: The Use of Some of the Information Criterion in Determining the Best Model for Forecasting of Thalassemia Cases Depending on Iraqi Patient Data Using ARIMA Model

  AUTHORS: Rana Sabeeh Abbood Alsudani, Jicheng Liu

  KEYWORDS: Thalassemia, The Information Criterion, Box-Jenkins Methodology

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.5 No.3, March 24, 2017

  ABSTRACT: Thalassemia is a major health problem in Iraq, and despites a prevention programme. There has been no decrease in the prevalence of the disease, due to a lack of awareness, implying that genetic counseling was a failure. This failure has been attributed to a lack of recognition of problems related to Thalassemia, unorganised teamwork and services, lack of knowledge and insufficient numbers of extension workers, lack of Thalassemia support groups, and inadequate research in Thalassemia prevention and control. Autoregressive Integrated Moving Average (ARIMA) model and forecasting has become a major tool in different applications. The ARIMA model introduced by Box and Jenkins (1971) is among the most effective approaches for analysing time-series data. In this study, we used Box and Jenkins methodology to build an ARIMA model to forecast the number of people with Thalassemia, for the period from 2016-2018, from the data base from Maysan Health Center specific for Thalassemia the Maysan Provence, Iraq. After the model selection, the best model for forecasting was ARIMA (0, 1, 1) and of models were used for forecasting Thalassemia.