Nonlinear model validation using correlation tests

Billings, S.A.; Zhu, Quanmin

doi:10.1080/00207179408921513

Cited by 150 publications

(70 citation statements)

References 13 publications

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“…As expected, during intense magnetospheric activity, the model overestimated the magnitude of the sub-storm. This shows that the neural network structure is relevant to the system and that we do not need to increase the order of the model (Billings and Voon, 1986;Billings and Zhu, 1989).…”

Section: Al Prediction Resultsmentioning

confidence: 99%

On the prediction of the auroral westward electrojet index

Amariutei

Ganushkina

2012

Ann. Geophys.

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Abstract. An ARMAX based model, to forecast the evolution of the of AL index, is developed. The model has been trained and validated using neural networks with the half wave rectifier (V B s ) as input. It is shown that the model posses a good, reliable forecasting ability, including periods of intense geomagnetic activity. Prediction efficiency of the model is discussed in the context of 1 min resolution output smoothed over 7 min.

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Section: Al Prediction Resultsmentioning

confidence: 99%

On the prediction of the auroral westward electrojet index

Amariutei

Ganushkina

2012

Ann. Geophys.

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“…The inclusion of the noise terms can eliminate bias in estimating the coefficients. The final stage is model validation, using methods such as the correlation tests (Billings and Voon, 1986;Billings and Zhu, 1989) or model performance analysis. The full description of the NARMAX algorithm is very complex and, as such, it is beyond the scope of this paper.…”

Section: Narmax Modelmentioning

confidence: 99%

Online NARMAX model for electron fluxes at GEO

2015

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Abstract. Multi-input single-output (MISO) nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been derived to forecast the > 0.8 MeV and > 2 MeV electron fluxes at geostationary Earth orbit (GEO). The NARMAX algorithm is able to identify mathematical model for a wide class of nonlinear systems from input-output data. The models employ solar wind parameters as inputs to provide an estimate of the average electron flux for the following day, i.e. the 1-day forecast. The identified models are shown to provide a reliable forecast for both > 0.8 and > 2 MeV electron fluxes and are capable of providing real-time warnings of when the electron fluxes will be dangerously high for satellite systems. These models, named SNB 3 GEO > 0.8 and > 2 MeV electron flux models, have been implemented online at http://www.ssg.group.shef. ac.uk/USSW/UOSSW.html.

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“…To do this, the identified model was subjected to both correlation tests (Billings and Zhu, 1989) and the model predictive performance was analyzed in both time and frequency.…”

Section: Model Validationmentioning

confidence: 99%

Data derived NARMAX Dst model

et al. 2011

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Abstract. The NARMAX OLS-ERR methodology is applied to identify a mathematical model for the dynamics of the Dst index. The NARMAX OLS-ERR algorithm, which is widely used in the field of system identification, is able to identify a mathematical model for a wide class of nonlinear systems using input and output data. Solar windmagnetosphere coupling functions, derived from analytical or data based methods, are employed as the inputs to such models and the outputs are geomagnetic indices. The newly deduced coupling function, p 1/2 V 4/3 B T sin 6 (θ/2), has been implemented as an input to model the Dst dynamics. It was shown that the identified model has a very good forecasting ability, especially with the geomagnetic storms.

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Nonlinear model validation using correlation tests

Cited by 150 publications

References 13 publications

On the prediction of the auroral westward electrojet index

On the prediction of the auroral westward electrojet index

Online NARMAX model for electron fluxes at GEO

Data derived NARMAX Dst model

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