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Nonlinear feature selection for support vector quantile regression

NEURAL NETWORKS 2025年 185卷 107136页

作者： Ye, Ya-Fen Wang, Jie Chen, Wei-Jie Zhejiang Univ Technol Sch Econ Hangzhou 310023 Peoples R China Zhejiang Univ Technol Inst Ind Syst Modernizat Hangzhou 310023 Peoples R China Zhejiang Univ Technol Zhijiang Coll Shaoxing 312030 Peoples R China

This paper discusses the nuanced domain of nonlinear feature selection in heterogeneous systems. To address this challenge, we present a sparsity-driven methodology, namely nonlinear feature selection for support vector quantile regression (NFS-SVQR). This method includes a binary-diagonal matrix, featuring 0 and 1 elements, to address the complexities of feature selection within intricate nonlinear systems. Moreover, NFS-SVQR integrates a quantile parameter to effectively address the intrinsic challenges of heterogeneity within nonlinear feature selection processes. Consequently, NFS-SVQR excels not only in precisely identifying representative features but also in comprehensively capturing heterogeneous information within high-dimensional datasets. Through feature selection experiments the enhanced performance of NFS-SVQR in capturing heterogeneous information and selecting representative features is demonstrated.

关键词： Sparse learning support vector quantile regression Nonlinear feature selection Mixed-integer optimization

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support vector quantile regression with varying coefficients

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COMPUTATIONAL STATISTICS 2016年第3期31卷 1015-1030页

作者： Shim, Jooyong Hwang, Changha Seok, Kyungha Inje Univ Dept Stat Kimhae 621749 Obang Dong South Korea Inje Univ Inst Stat Informat Kimhae 621749 Obang Dong South Korea Dankook Univ Dept Appl Stat Yongin 448701 Gyeonggido South Korea

quantile regression has received a great deal of attention as an important tool for modeling statistical quantities of interest other than the conditional mean. Varying coefficient models are widely used to explore dynamic patterns among popular models available to avoid the curse of dimensionality. We propose a support vector quantile regression model with varying coefficients and its two estimation methods. One uses the quadratic programming, and the other uses the iteratively reweighted least squares procedure. The proposed method can be applied easily and effectively to estimating the nonlinear regression quantiles depending on the high-dimensional vector of smoothing variables. We also present the model selection method that employs generalized cross validation and generalized approximate cross validation techniques for choosing the hyperparameters, which affect the performance of the proposed model. Numerical studies are conducted to illustrate the performance of the proposed model.

关键词： Generalized approximate cross validation Generalized cross validation Iteratively reweighted least squares Hyperparameter selection Quadratic programming quantile regression support vector machine support vector quantile regression Varying coefficient model

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Short-term power load probability density forecasting method using kernel-based support vector quantile regression and Copula theory

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APPLIED ENERGY 2017年第Part1期185卷 254-266页

作者： He, Yaoyao Liu, Rui Li, Haiyan Wang, Shuo Lu, Xiaofen Hefei Univ Technol Sch Management Hefei 230009 Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Hefei 230009 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

Penetration of smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems. Probability density forecasting methods can effectively quantify the uncertainty of power load forecasting. The paper proposes a short-term power load probability density forecasting method using kernel-based support vector quantile regression (KSVQR) and Copula theory. As the kernel function can influence the prediction performance, three kernel functions are compared in this work to select the best one for the learning target. The paper evaluates the accuracy of the prediction intervals considering two criteria, prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW). Considering uncertainty factors and the correlation of explanatory variables for power load prediction accuracy are of great importance. A probability density forecasting method based on Copula theory is proposed in order to achieve the relational diagram of electrical load and real-time price. The electrical load forecast accuracy of the proposed method is assessed by means of real datasets from Singapore. The simulation results show that the proposed method has great, potential for power load forecasting by selecting appropriate kerhel function for KSVQR model. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Short-term power load probability density forecasting support vector quantile regression PI coverage probability PI normalized average width Copula theory Real-time price

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Monotone support vector quantile regression

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2017年第10期46卷 5180-5193页

作者： Shim, Jooyong Seok, Kyungha Hwang, Changha Inje Univ Inst Stat Informat Dept Stat Kimhae South Korea Dankook Univ Dept Appl Stat Gyeonggido South Korea

quantile regression (QR) models have received a great deal of attention in both the theoretical and applied statistical literature. In this paper we propose support vector quantile regression (SVQR) with monotonicity restriction, which is easily obtained via the dual formulation of the optimization problem. We also provide the generalized approximate cross validation method for choosing the hyperparameters which affect the performance of the proposed SVQR. The experimental results for the synthetic and real data sets confirm the successful performance of the proposed model.

关键词： Generalized approximate cross validation monotonicity quantile regression support vector machine support vector quantile regression 62G08 Nonparametric regression

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Short-term Electricity Price Probabilistic Forecasting Based on support vector quantile regression Optimized by Simulated Annealing Algorithm

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2021年第2期14卷 156-170页

作者： He, Hui Zhang, Rui Li, Kaihang Jie, Yongjun Jiao, Runhai Chen, Bo North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China China Unicom Big Data Co Ltd Beijing 100011 Peoples R China

Background: Electricity price forecasting is still a challenging issue as it plays an essential role in balancing electricity generation and consumption. Probabilistic electricity price forecasting not only provides deterministic price forecasts but also effectively quantifies the uncertainty of electricity price. Methods: This paper introduces a new short-term electricity price forecasting approach called SASVQR, which is based on support vector quantile regression (SVQR) optimized by simulated annealing algorithm. In this study, SVQR is employed to obtain the conditional quantiles of the electricity under different quantile points, while the simulated annealing algorithm is applied to optimize each SVR model. Then the kernel density estimation takes these conditional quantiles as inputs and generates the probability density functions for future electricity prices. Results: The proposed algorithm is assessed in three datasets: the GEFCom 2014, two real electricity price datasets from the PJM market and the Singapore market. Three popular probabilistic forecasting criteria, namely prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width-based criterion (CWC), are utilized to evaluate the numerical experiment results. It shows the promising forecasting performance, robustness, and effectiveness of SASVQR on different datasets. Conclusion: The SASVQR method can effectively forecast the short-term electricity price compared with other methods.

关键词： Electricity price probabilistic forecasting support vector quantile regression simulated annealing algorithm kernel density estimation quantile regression

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support vector quantile regression using asymmetric e-insensitive loss function

Support vector quantile regression using asymmetric e-insens...

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2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Daehyeon Cho Kyung Ha Seok Changha Hwang Jooyong Shim Department of Data Science and Institute of Statistical Information lnje University Kimhae 621-749 Department of Statistics Dankook University Yongin-si Gyeonggi-do 448-701 Korea Department of Applied Statistics Catholic University of Daegu Gyungbuk 702-701 Korea

ISBN: (纸本)9781424463671;9781424463688;9781424463701

support vector quantile regression (SVQR) is capable of providing a good description of the linear and nonlinear relationships among random variables. In this paper we propose a sparse SVQR to overcome a weak point of SVQR, nonsparsity. The asymmetric einsensitive loss function is used to efficiently provide the sparsity Experimental results are then presented;these results illustrate the performance of the proposed method by comparing it with nonsparse SVQR.

关键词： quantile regression sparsity support vectors support vector quantile regression

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Estimating small area mean with mixed and fixed effects support vector median regressions

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NEUROCOMPUTING 2014年 145卷 174-181页

作者： Shim, Jooyong Hwang, Changha Inje Univ Dept Data Sci Inst Stat Informat Kyungnam 621749 South Korea Dankook Univ Dept Appl Stat Gyeonggido 448160 South Korea

Small area estimation has been extensively studied under linear mixed effects models. However, when the functional form of the relationship between the response and the covariates is not linear, it may lead to biased estimators of the small area parameters. In this paper, we relax the assumption of linear regression for the fixed part of the model and replace it by using the underlying concept of support vector quantile regression. This makes it possible to express the nonparametric small area estimation problem as mixed or fixed effects model regression. Through numerical studies we compare the efficiency of different models in estimating small area mean. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Fixed effect Median regression Mixed effect Semiparametric model Small area estimation support vector quantile regression

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Applying regression techniques in designing optimal trade execution strategy for an asset

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OPTIMIZATION 2022年第3期71卷 463-484页

作者： Sehgal, Ruchika Mehra, Aparna Indian Inst Technol Delhi Dept Math New Delhi India

The paper aims to construct an optimal trading strategy for procuring a large but fixed volume of a risky asset. The proposed approach splits a large block of trade into smaller packages to minimize the execution cost of the trade. In this study, we suggest an incipient price dynamics for the asset where we express its current market price as a convex combination of the price impervious to our anterior trade in the asset and the execution price carrying the impact of our anterior trade in it. We propose to apply the regression techniques to estimate the unaffected price of the asset. The formulated model is a convex quadratic optimization problem and thus computationally tractable. We evaluate the performance of the proposed model on five stocks namely, Apple, Microsoft, Coca-Cola, Amazon, and Netflix and conclude that the proposed model consistently achieves a lower execution cost than the one obtained from some other models existing in the literature.

关键词： Optimal trade execution problem price dynamics of an asset quadratic programming problem quantile regression support vector quantile regression

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Kernel-based orthogonal quantile regression model

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Model Assisted Statistics and Applications 2017年第3期12卷 217-226页

作者： Shim, Jooyong Hwang, Changha Department of Statistics Institute of Statistical Information Inje University Kimhae South Korea Department of Applied Statistics Dankook University Gyeonggido 448-160 South Korea

quantile regression models with errors in variables have received a great deal of attention in the social and natural sciences. Some efforts have been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose a kernel-based orthogonal quantile regression model that effectively considers the errors on both input and response variables. We also provide a generalized cross validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed models. The proposed method is evaluated through simulations. © 2017 IOS Press and the authors.

关键词： Errors-in-variables generalized cross validation kernel measurement error orthogonal residual quantile regression support vector machine support vector quantile regression

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Optimal Dispatching of Offshore Microgrid Considering Probability Prediction of Tidal Current Speed

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ENERGIES 2019年第17期12卷 3384-3384页

作者： Zhang, Anan Sun, Yangfan Yang, Wei Huang, Huang Feng, Yating Southwest Petr Univ Sch Elect & Informat Engn Chengdu 610500 Sichuan Peoples R China Cardiff Univ Sch Engn Cardiff CF24 3AA S Glam Wales

Oceans contain rich tidal current energy, which can provide sufficient power for offshore microgrids. However, the uncertainty of tidal flow may endanger the operational reliability of an offshore microgrid. In this paper, a probabilistic prediction model of tidal current is established based on support vector quantile regression to reduce the influence of uncertainty. Firstly, the penalty factors and kernel parameters of the proposed prediction model was optimized by the dragonfly algorithm to predict the tidal speed of any time of a day in different quantiles. Secondly, combining the above result with the kernel density to predict the probability density function of the tidal current speed, which is to improve the accuracy of prediction in the absence of information. Thirdly, an optimal generation dispatching strategy with tidal current generators is proposed to minimize the fuel consumption of offshore microgrids. Finally, a case study based on the offshore oil and gas platform in Bohai shows that the mean absolute percent error of the proposed model is 2.8142%, which is better than support vector quantile regression model and support vector regression model optimized by the genetic algorithm.

关键词： offshore microgrids support vector quantile regression dragonfly algorithm probability prediction optimal operation

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