BibTex Citation Data :
@article{Medstat7639, author = {Dian Anggraeni and Alan Prahutama and Shofi Andari}, title = {APLIKASI GENERALIZED SPACE TIME AUTOREGRESSIVE (GSTAR) PADA PEMODELAN VOLUME KENDARAAN MASUK TOL SEMARANG}, journal = {MEDIA STATISTIKA}, volume = {6}, number = {2}, year = {2013}, keywords = {}, abstract = { Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (2 1 )-I(1,12) uniformly weighted with the smallest REMSE mean 76834. Key words: GSTAR, Vehicle Volume, Space-Time Model}, issn = {2477-0647}, pages = {61--70} doi = {10.14710/medstat.6.2.61-70}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/7639} }
Refworks Citation Data :
Time series data from neighboring separated location often associated both spatially and through time. Generalized space time autoregrresive (GSTAR) model is one of the most common used space-time model to modeling and predicting spatial and time series data. This study applied GSTAR to modeling vehicle volume entering four tollgate (GT) in Semarang City: GT Muktiharjo, GT Gayamsari, GT Tembalang, and GT Manyaran. The data was collected by month from 2003 to 2009. The best model provided by this study is GSTAR (21)-I(1,12) uniformly weighted with the smallest REMSE mean 76834.
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