In most meteorological problems, two or more variables evolve over time. These variables not only haverelationships with each other, but also depend on each other. Although in many situations the interest was onmodelling single variable as a vector time series without considering the impact other variables have on it. Thevector autoregression (VAR) approach to multiple time series analysis are potentially useful in many types ofsituations which involve the building of models for discrete multivariate time series. This approach has 4important stages of the process that are data pre-processing, model identification, parameter estimation, andmodel adequacy checking. In this research, VAR modeling strategy was applied in modeling three variables ofmeteorological variables, which include temperature, wind speed and rainfall data. All data are monthly data,taken from the Kuala Krai station from January 1985 to December 2009. Two models were suggested byinformation criterion procedures, however VAR (3) model is the most suitable model for the data sets based onthe model adequacy checking and accuracy testing.
Recursive Subspace Model Identification Based On Vector Autoregressive Modelling
