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http://dx.doi.org/10.7465/jkdi.2017.28.1.11

Prediction for spatial time series models with several weight matrices  

Lee, Sung Duck (Department of Information and Statistics, Chungbuk National University)
Ju, Su In (Department of Information and Statistics, Chungbuk National University)
Lee, So Hyun (Department of genome Epidemiology, Korea National Institute of Health)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.1, 2017 , pp. 11-20 More about this Journal
Abstract
In this paper, we introduced linear spatial time series (space-time autoregressive and moving average model) and nonlinear spatial time series (space-time bilinear model). Also we estimated the parameters by Kalman Filter method and made comparative studies of power of forecast in the final model. We proposed several weight matrices such as equal proportion allocation, reciprocal proportion between distances, and proportion of population sizes. For applications, we collected Mumps data at Korea Center for Disease Control and Prevention from January 2001 until August 2008. We compared three approaches of weight matrices using the Mumps data. Finally, we also decided the most effective model based on sum of square forecast error.
Keywords
Kalman filter; Mumps; SSF; STARMA model; STBL model; weight matrix;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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