Meteorological multivariable approximation and prediction with classical VAR-DCC approach
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 t...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2018
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/85716/ http://dx.doi.org/10.17576/jsm-2018-4702-24 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Malaysia |
| Summary: | 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. |
|---|