BibTex Citation Data :
@article{J.Gauss10237, author = {Ronny Gusnadi and Rita Rahmawati and Alan Prahutama}, title = {PEMODELAN GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) SEASONAL PADA DATA JUMLAH WISATAWAN MANCANEGARA EMPAT KABUPATEN/KOTA DI JAWA TENGAH}, journal = {Jurnal Gaussian}, volume = {4}, number = {4}, year = {2015}, keywords = {GSTAR, RMSE, International Tourist}, abstract = { In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling number of international tourist four locations in Magelang Regency, Surakarta City, Wonosobo Regency, and Karanganyar Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR(1 1 )-I(1) 12 with the inverse distance weighted. Based on GSTAR(1 1 )-I(1) 12 with the inverse distance weighted, the relationship between the location shown on International tourist arrivals Surakarta City influenced by the International tourist in other regencies. Keywords : GSTAR, RMSE, International Tourist }, issn = {2339-2541}, pages = {1017--1026} doi = {10.14710/j.gauss.4.4.1017-1026}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/10237} }
Refworks Citation Data :
In many applications, several time series data are recorded simultaneously at a number of locations. Time series data from nearby locations often to be related by spatial and time. This data is called spatial time series data. Generalized Space Time Autoregressive (GSTAR) model is one of space time models used to modeling and forecasting spatial time series data. This study applied GTSAR model to modeling number of international tourist four locations in Magelang Regency, Surakarta City, Wonosobo Regency, and Karanganyar Regency. Based on the smallest RMSE mean of forecasting result, the best model chosen by this study is GSTAR(11)-I(1)12 with the inverse distance weighted. Based on GSTAR(11)-I(1)12 with the inverse distance weighted, the relationship between the location shown on International tourist arrivals Surakarta City influenced by the International tourist in other regencies.
Keywords: GSTAR, RMSE, International Tourist
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