| dc.description.abstract | Recent research in time series shows that the data not only have inter-relations with
events in the previous time, but also have inter-location linkages. This type of time series data
with elements of time and location dependencies are modeled with the Space-Time model. The
Space-Time model with heterogeneous research sites is the Generalized Space Time
Autoregressive (GSTAR) model. The data that has a seasonal pattern is modeled with seasonal
GSTAR by including seasonal elements in the non-seasonal model. In this case, Jember
District has 77 rain stations with various regional topography. Based on various characteristics,
K-Means cluster analysis is used to obtain optimal rainfall rain stations clusters. This clustering
is expected to give better rainfall forecasting result compared with clustering conducted by the
Statistics Central of Jember (BPS). The RMSE value can be minimized by including seasonal
elements in the model, both in BPS and K-Means clustering. In addition, the K-Means
clustering in this study may also reduce the RMSE value of model, both on non-seasonal and
seasonal models. The best model for this case is GSTARK-Seasional (1;1) , ie Seasonal
GSTAR model on K-Means clustering. | en_US |
