Online support vector quantile regression for the dynamic time series with heavy-tailed noise

Ye, Yafen; Shao, Yuan‐Hai; Li, Chunna; Hua, Xiang‐Yu; Guo, Yafei

doi:10.1016/j.asoc.2021.107560

Cited by 10 publications

(2 citation statements)

References 23 publications

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“…First, FS-NSVR is not suitable for the heterogeneity problem of nonlinear high-dimensional data. Te spirit of quantile regression [35][36][37] can be brought into the nonlinear feature selection framework in the future. Second, more efcient methods to solve FS-NSVR are needed since the training speed of the current method is not fast enough with regard to large-scale data sets.…”

Section: Discussionmentioning

confidence: 99%

Novel Feature Selection Method for Nonlinear Support Vector Regression

Chen

2022

Complexity

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The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve the nonlinear feature selection problem in the regression technology field. FS-NSVR preserves the representative features selected in the complex nonlinear system due to its use of a feature selection matrix in the original space. FS-NSVR is a challenging mixed-integer programming problem that is solved efficiently by using an alternate iterative greedy algorithm. Experimental results on three artificial datasets and five real-world datasets confirm that FS-NSVR effectively selects representative features and discards redundant features in a nonlinear system. FS-NSVR outperforms L1-norm support vector regression, L1-norm least squares support vector regression, and Lp-norm support vector regression on both feature selection ability and regression efficiency.

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Section: Discussionmentioning

confidence: 99%

Novel Feature Selection Method for Nonlinear Support Vector Regression

Chen

2022

Complexity

Self Cite

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“…One of the supervised learning models for classification and regression is the support vector machine (SVM). Regression using a support vector machine is known as a support vector regression (SVR) [30][31]. So, SVR is one of the most reliable machine learning or statistical learning algorithms.…”

Section: A Support Vector Regression (Svr) Modelmentioning

confidence: 99%

Faults prediction and monitoring of complex processes using an ensemble of machine learning regression models: application to the Tennessee Eastman Process

Rezgui

Rezki²,

Kerrouchi³

2023

Preprint

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Modern industrial installations are a source of big amount of data, these data serve as a means of monitoring and control and can also be used for the prediction of the parameters characterizing the supervised process and thus the anomalies. For this, there are several machine learning regression models that can be considered in order to select the best prediction tool. Our study consists in selecting the best means of prediction of the different operating parameters of the Tennessee Eastman (TEP) process. Indeed, this consists in selecting the best model that can ensure an efficient and cost-effective prognosis and predictive monitoring system. Four regression models were considered during our comparative study: the Support Vector Regression (SVR), Gaussian Process Regression (GPR), Decision Tree Regression (DTR) and Ensemble of Learners approaches for Regression (ELR). As evaluation criteria, we chose the MSE regression error by re-substitution, the MSE regression loss for the cross-validation kernel, the optimization of the hyper-parameters and the training time.

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Probabilistic quantile multiple fourier feature network for lake temperature forecasting: incorporating pinball loss for uncertainty estimation

Liu,

Deng,

Yuan

et al. 2024

Earth Sci Inform

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Lake temperature forecasting is crucial for understanding and mitigating climate change impacts on aquatic ecosystems. The meteorological time series data and their relationship have a high degree of complexity and uncertainty, making it difficult to predict lake temperatures. In this study, we propose a novel approach, Probabilistic Quantile Multiple Fourier Feature Network (QMFFNet), for accurate lake temperature prediction in Qinghai Lake. Utilizing only time series data, our model offers practical and efficient forecasting without the need for additional variables. Our approach integrates quantile loss instead of L2-Norm, enabling probabilistic temperature forecasts as probability distributions. This unique feature quantifies uncertainty, aiding decision-making and risk assessment. Extensive experiments demonstrate the method’s superiority over conventional models, enhancing predictive accuracy and providing reliable uncertainty estimates. This makes our approach a powerful tool for climate research and ecological management in lake temperature forecasting. Innovations in probabilistic forecasting and uncertainty estimation contribute to better climate impact understanding and adaptation in Qinghai Lake and global aquatic systems.

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Online support vector quantile regression for the dynamic time series with heavy-tailed noise

Cited by 10 publications

References 23 publications

Novel Feature Selection Method for Nonlinear Support Vector Regression

Novel Feature Selection Method for Nonlinear Support Vector Regression

Faults prediction and monitoring of complex processes using an ensemble of machine learning regression models: application to the Tennessee Eastman Process

Probabilistic quantile multiple fourier feature network for lake temperature forecasting: incorporating pinball loss for uncertainty estimation

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