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Kalman-Filter Estimation and Prediction for a Spatial Time Series Model
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 Title & Authors
Kalman-Filter Estimation and Prediction for a Spatial Time Series Model
Lee, Sung-Duck; Han, Eun-Hee; Kim, Duck-Ki;
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A spatial time series model was used for analyzing the method of spatial time series (not the ARIMA model that is popular for analyzing spatial time series) by using chicken pox data which is a highly contagious disease and grid data due to ARIMA not reflecting the spatial processes. Time series model contains a weighting matrix, because that spatial time series model influences the time variation as well as the spatial location. The weighting matrix reflects that the more geographically contiguous region has the higher spatial dependence. It is hypothesized that the weighting matrix gives neighboring areas the same influence in the study of the spatial time series model. Therefore, we try to present the conclusion with a weighting matrix in a way that gives the same weight to existing neighboring areas in the study of the suitability of the STARMA model, spatial time series model and STBL model, in the comparative study of the predictive power for statistical inference, and the results. Furthermore, through the Kalman-Filter method we try to show the superiority of the Kalman-Filter method through a parameter assumption and the processes of prediction.
STARMA;STBL;weight matrix;STACF;STPACF;Newton-Raphson;Kalman-Filter;SSF;
 Cited by
태양광발전 단기예측모델 개발,김광득;

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