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Periodic transfer function-noise model for forecasting

journal contribution
posted on 06.12.2017, 00:00 by Mohammad MondalMohammad Mondal, Saleh WasimiSaleh Wasimi
A new class of time series models, referred to in this paper as the "periodic transfer function-noise (PTFN) model," has been developed through an extension of conventional nonperiodic (or constant parameter) transfer function-noise (TFN) models. The proposed PTFN model is very flexible, as its form or order and parameter values of both the dynamic and noise components may vary depending on the season of the year. It is shown that Box et al. 's modeling techniques for TFN models can be applied to PTFN models as well. The model has been applied for monthly forecasting of the Ganges River flow using monthly rainfall data of northern India as the predictor. The results are encouraging and suggest that the PTFN class of models has the potential to be useful in capturing the seasonally varying dynamic relationship between a dependent time series and one or more independent time series where each series is interyear stationary but within-year nonstationary.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Volume

10

Issue

5

Start Page

353

End Page

362

Number of Pages

10

ISSN

1084-0699

Location

Reston, Virginia

Publisher

American Society of Civil Engineers

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Bangladesh University of Engineering and Technology; Faculty of Informatics and Communication; TBA Research Institute;

Era Eligible

Yes

Journal

Journal of hydrologic engineering.

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