On the identification problem for bilinear time series models

Wu, Berlin; Shih, Neng-Hui

doi:10.1080/00949659208811435

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Suba Rao and Gabr (1984) have proposed a method about model identification and parameters estimation of BL (p, O, m, n), but it takes a lot of computations and iterations. Wu and Shih (1992) suggest a more efficient process than Subba Rao and Gabr's method for simple bilinear models. However, for general bilinear time series, there is no consistent and efficient method for solving the identification problem so far.…”

Section: Neural Network and Model-free Forecastingmentioning

confidence: 95%

Identification environment and robust forecasting for nonlinear time series

1994

Comput Econ

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Abslract. In this paper, the methods of time series for nonlinearity are briefly surveyed, with particular attention paid to a new test design based on a neural network specification. The proposed integrated expert system contains two main components: an identification environment and a robust forecasting design. The identification environment can be viewed as a integrated dynamic design in which cognitive capabilities arise as a direct consequence of their self-organizational properties. The integrated framework used for discussing the similarities and differences in the nonlinear time series behavior is presented. Moreover, its performance in prediction proves to be superior than the former work. For the investigation of robust forecasting, we perform a simulation study to demonstrate the applicability and the forecasting performance.

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Section: Neural Network and Model-free Forecastingmentioning

confidence: 95%

Identification environment and robust forecasting for nonlinear time series

1994

Comput Econ

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“…However, a great number of fluctuations in data can be detected when using this model. Further applications were developed by Weiss (1986), Wu and Shih (1992), Wu and Hung (1999), and Chen and Wu (2001). Engle (1982) first proposed the ARCH (autoregressive conditional heteroscedasticity) model.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear time series analysis using two‐stage genetic algorithms for streamflow forecasting

Chen

Liu

2008

Hydrological Processes

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Abstract:Streamflow forecasting is very important for the management of water resources: high accuracy in flow prediction can lead to more effective use of water resources. Hydrological data can be classified as non-steady and nonlinear, thus this study applied nonlinear time series models to model the changing characteristics of streamflows. Two-stage genetic algorithms were used to construct nonlinear time series models of 10-day streamflows of the Wu-Shi River in Taiwan. Analysis verified that nonlinear time series are superior to traditional linear time series. It is hoped that these results will be useful for further applications.

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On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

Chen

Cherng

2001

Computational Statistics

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On the identification problem for bilinear time series models

Cited by 5 publications

References 10 publications

Identification environment and robust forecasting for nonlinear time series

Identification environment and robust forecasting for nonlinear time series

A nonlinear time series analysis using two‐stage genetic algorithms for streamflow forecasting

On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach

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