GSTAR MODELLING TO FORECAST MANY VEHICLE ENTERING PURBALEUNYI TOLL GATES WITH SOME WEIGHT MATRIX PERSPECTIVE
Weight Matrix is one of the most important thing to use Generalized Space Time Autoregressive (GSTAR) modeling. But, usually, weight matrix built based on assumption or subjectivity of the researcher. Minimum Spanning Tree (MST) can be one of the alternative to build matrix weight based on our ow...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/39155 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Weight Matrix is one of the most important thing to use Generalized Space Time
Autoregressive (GSTAR) modeling. But, usually, weight matrix built based on
assumption or subjectivity of the researcher. Minimum Spanning Tree (MST) can be
one of the alternative to build matrix weight based on our own data. In this final
project, many vehicles entering purbaleunyi toll modeled by GSTAR with some weight
matrix perspective. According to STACF-STPACF graph, derived some suitable
models, such as : GSTAR(1;1) modeling, GSTAR(1;2) modeling, GSTAR(2;1,1)
modeling. Therefore, from comparison between root mean square error, Akaike’s
Information Criterion (AIC), and Bayesian Information Criterion(BIC), we conclude
that GSTAR(1;1) with matrix weight based on radius distance is the best model to
forecast many vehicles entering purbaleunyi toll. |
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