Periodic transfer function-noise model for forecasting
journal contribution
posted on 2017-12-06, 00:00authored byMohammad Mondal, Saleh 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;