Author, Subjects, Keywords

Cited Author

 
 
   » By Author or Editor
 » Browse Author by Alphabet
 » By Journal
 » By Subjects
 » By Affiliations
 » By Type
 » By Year
 » By Latest Additions
 
 
   » By Author
 » Top 20 Authors
 » Top 20 Article
 » Top 20 Journal Cited
 » Top 20 Cited
 » Top 20 Author Cited
 » Usage Since Sept 2007


 
 
 

Login | Create Account

Water Level Data Modeling with Bilinear Time Series Analysis

Mohd. Sahar Yahya, and Ibrahim Mohamed, and Azami Zaharim, and Mohammad Said Zainol, (2006) Water Level Data Modeling with Bilinear Time Series Analysis. Malaysian Journal of Science, 25 (1). pp. 73-78. ISSN 13943065

Full text not available from this repository.

Official URL: http://ejum.fsktm.um.edu.my

Affiliations

University of Malaya. Centre for Foundation Studies in Science.
University of Malaya. Inst. of Mathematical Sciences.
National University of Malaysia. Faculty of Engineering. Dept. of Architecture.
MARA University of Technology. Faculty of Information Technology & Quantitative Sciences.

Abstract

In the literature, many time series data, such as the economic and hydrological data, show various nonlinearity characteristics. The Keenan's test and F-test are employed in identifying a nonlinear data set. This article looks at the modeling of nonlinear time series data using bilinear time series model. The model is an extension of autoregressive model such that an extra term representing the bilinear characteristic is introduced. The estimation of bilinear models is obtained using nonlinear least squares method. As an illustration, analysis on water level of Sungai Kelantan using the above method is presented.

Item Type:Journal
Keywords:Bilinear, nonlinear least squares method, hydrology
Subjects:Q Science, Computer Science
T Technology, Engineering
N Architecture, Fine Arts, Performing Arts
ID Code:2356

1. Box, G.E.P. and Jenkins, G.M. (1976). Time Series Analysis Forecasting and Control. Holden-Day, San Francisco.

2. Fuller, W.A. (1976). Introduction to Statistical Time Series. Wiley, New York.

3. Chatfield, C. (1996). The Analysis of Time Series: An Introduction. Chapman and Hall, London.

4. Oyet, A.J. (2001). Nonlinear time series modeling: Order identification and wavelet filtering. Interstat, April 2001.

5. Lewis, P.A.W. and Ray, B.K. (2002). Nonlinear modeling of periodic threshold autoregressions using TSMARS. Journal of Time Series Analysis 23 (4): 272 - 285.

6. Akaike, H. (1969). Fitting autoregressive model for prediction. Annals Institute of Statistical Mathematics 21: 203 - 217.

7. Akaike, H. (1979). A Bayesian extension of the minimum AIC procedure of autoregressive modeling. Biometrika 66: 237 - 242.

8. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6: 461 - 464.

9. Keenan, D. M. (1985). A Tukey non-additivity type test for time series nonlinearity. Biometrika 72 (1): 39 - 44.

10. Tsay, R.S. (1986). Nonlinearity test for time series. Biometrika 73 (2): 461 - 466.

11. Priestly, M.B. (1991). Non-linear and Non-stationary Time Series Analysis. Academic Press, San Diego.

Repository Staff Only: item control page
 
 
 
 
 
   
© Copyright 2007 MyAIS Faculty of Computer Science and Information Technology, University of Malaya – All Rights Reserved. Malaysian Abstracting and Indexing System is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.