NDLI: Hybridization of Support Vector Regression and Firefly Algorithm for Diarrhoeal Outpatient Visits Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Yongming Wang Junzhong Gu
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China (Yongming Wang; Junzhong Gu)
Abstract	Accurate and reliable forecasts of diarrhoeal outpatient visits are necessary for the health authorities to ensure the appropriate action for the control of the outbreak. In this study, a novel forecasting model based on hybridization the Firefly Algorithm (FA) and Support Vector Regression (SVR) has been proposed to forecast the diarrhoeal outpatient visits in Shanghai. The performance of SVR models depends upon the appropriate choice of SVR parameters. In this study, FA has been employed for determining the parameters. The rainfall, temperature, relative humidity and diarrhoeal outpatient visits have been considered as input variables. Time series of diarrhoeal outpatient visits of children and adult has been obtained for a period of January 2006 to December 2011. Further, the rainfall, relative humidity and temperature data have been obtained from meteorological records. The performance of the proposed SVR-FA model has been compared with Multivariable Linear Regression (MRL) method, Artificial Neural Networks (ANNs) and also with SVR. The results indicate that the proposed model performs best based on two error measures, namely mean squared error (RMSE) and mean absolute percent error (MAPE).
Starting Page	70
Ending Page	74
File Size	848795
Page Count	5
File Format	PDF
ISBN	9781479965724
ISSN	10823409
DOI	10.1109/ICTAI.2014.21
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-10
Publisher Place	Cyprus
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Mathematical model Computational modeling Artificial neural networks Forecasting Testing Support vector machines time series forecasting support vector regression firefly algorithm diarrhoeal outpatient visits
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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