Spatial Weighting Selection in GSTAR and S-GSTAR Models for Temperature Prediction
DOI:
https://doi.org/10.20956/j.v20i3.34305Keywords:
Space-time, GSTAR, seasonal, temperature,, predictionAbstract
Recent research in time series analysis indicates that events at a particular location are not only influenced by events at previous times but also by proximity between locations. Events influenced by both space and time can be modeled using a space-time model. GSTAR model is one such space-time model. In its development, time series data exhibiting seasonal patterns are modeled using Seasonal GSTAR (S-GSTAR). The GSTAR and S-GSTAR models are used to model temperature in the Banjar, Cilacap, and Sleman Districts. The purpose of employing both methods is to compare the best model for modeling temperature at these three locations. Spatial weights used include inverse distance weighting using the Euclidean distance formula, uniform weighting, and cross-correlation normalization weighting. Ordinary Least Squares (OLS) is the estimation method used in this study. The best model obtained is S-GSTAR with inverse distance weighting, as this model has the smallest RMSE value.
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