The vector autoregressive (VAR) approach is useful in many situations involving model development for multivariables
time series. VAR model was utilised in this study and applied in modelling and forecasting four meteorological variables.
The variables are n rainfall data, humidity, wind speed and temperature. However, the model failed to address the
heteroscedasticity problem found in the variables, as such, multivariate GARCH, namely, dynamic conditional correlation
(DCC) was incorporated in the VAR model to confiscate the problem of heteroscedasticity. The results showed that the use
of the VAR coupled with the recognition of time-varying variances DCC produced good forecasts over long forecasting
horizons as compared with VAR model alone.
Simulation is used to measure the robustness and the efficiency of the forecasting
techniques performance over complex systems. A method for simulating multivariate
time series was presented in this study using vector autoregressive base-process. By
applying the methodology to the multivariable meteorological time series, a simulation
study was carried out to check for the model performance. MAPE and MAE performance
measurements were used and the results show that the proposed method that consider
persistency in volatility gives better performance and the accuracy error is six time smaller
than the normal hybrid model.