Sains Malaysiana 47(2)(2018): 409-417

http://dx.doi.org/10.17576/jsm-2018-4702-24

Meteorological Multivariable Approximation and Prediction with Classical VAR-DCC Approach

(Penghampiran Berbilang Pemboleh Ubah Meteorologi dan Jangkaan dengan Pendekatan Klasik VAR-DCC)

 

Siti Mariam Norrulashikin¹, Fadhilah Yusof¹* & Ibrahim Lawal Kane²

 

¹Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor Darul Takzim, Malaysia

 

²Department of Mathematical and Computer Science, Umaru Musa Yar'adua University, Katsina State, Nigeria

 

Diserahkan: 7 Februari 2017/Diterima: 5 Julai 2017

ABSTRACT

 

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.

Keywords: Dynamic conditional correlation; forecast; meteorology; vector autoregressive

 

 

ABSTRAK

 

Pendekatan vektor autoregresif (VAR) adalah berguna dalam pelbagai keadaan yang melibatkan pembangunan model berbilang siri masa pemboleh ubah. Model VAR digunakan dalam kajian ini dan diaplikasi dalam pemodelan dan peramalan empat pemboleh ubah meteorologi. Pemboleh ubah ini adalah data hujan n, kelembapan, kelajuan angin dan suhu. Walau bagaimanapun, model ini gagal untuk menangani masalah heteroskedastisiti yang ditemui dalam pemboleh ubah, justeru, multivariat GARCH iaitu kolerasi dinamik bersyarat (DCC) telah dimasukkan pada model VAR untuk merampas masalah heteroskedastisiti. Keputusan menunjukkan bahawa penggunaan VAR ditambah pula dengan pengiktirafan daripada variasi perbezaan masa DCC menghasilkan peramalan yang baik ke atas peramalan panjangberbanding model VAR semata-mata.

Kata kunci: Korelasi dinamik bersyarat; meteorologi; ramalan; vektor autoregresif

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*Pengarang untuk surat-menyurat; email: fadhilahy@utm.my