Gaussian process for nonstationary time series prediction

Brahim-Belhouari, S.; Bermak, Amine

doi:10.1016/j.csda.2004.02.006

Cited by 241 publications

(105 citation statements)

References 1 publication

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“…Empirical comparative studies have confirmed the outstanding performance of Gaussian process regression with respect to other non-linear models [11,12,13]. As a result, Gaussian process models have been widely applied to various problems in statistics and engineering [14,11,15,16,17,18,13].…”

Section: Introductionmentioning

confidence: 81%

Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers

Chen

Wang

2010

Neurocomputing

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Section: Introductionmentioning

confidence: 81%

Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers

Chen

Wang

2010

Neurocomputing

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“…GP model is a flexible nonparametric model, which has been widely applied to multi-step-ahead predictions in time series analysis [25,34]. A GP model is completely specified by its mean function and covariance function.…”

Section: Gaussian Process Modelmentioning

confidence: 99%

“…It is described as: φ(t) = e c·t − e −c·t e c·t + e −c·t (34) where c is a length-scale parameter. The relationship between regeneration amplitude and DT (k) can be expressed by:…”

Section: Of 18mentioning

confidence: 99%

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

et al. 2016

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Abstract:State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

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“…It has been widely applied to multi-step-ahead predictions in time series analysis [26,33]. A GP model is completely specified by the mean function m(x) and the covariance function k(x, x ), where m(x) and k(x, x ) are described as follows:…”

Section: Gaussian Process Modelmentioning

confidence: 99%

State of Health Estimation of Li-ion Batteries with Regeneration Phenomena: A Similar Rest Time-Based Prognostic Framework

et al. 2016

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Abstract:State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

show abstract

Gaussian process for nonstationary time series prediction

Cited by 241 publications

References 1 publication

Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers

Bayesian variable selection for Gaussian process regression: Application to chemometric calibration of spectrometers

A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

State of Health Estimation of Li-ion Batteries with Regeneration Phenomena: A Similar Rest Time-Based Prognostic Framework

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