A class of new Support Vector Regression models

Anand, Pritam; Rastogi, Reshma; Chandra, Suresh

doi:10.1016/j.asoc.2020.106446

Cited by 30 publications

(15 citation statements)

References 22 publications

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“…For instance, we have a time-series data set where 𝑧 = (𝑥 𝑖 𝑦 𝑖 ), 1 ≤ 𝑖 ≤ 𝑁. The core idea dragged by [35]. Let suppose 𝑎 𝑚 = 𝑊 𝑡 , the problem may solve as follows:…”

Section: ) Support Vector Regressionmentioning

confidence: 99%

Evaluation Optimal Prediction Performance of MLMs on High-volatile Financial Market Data

Yao¹,

Naveed²,

Answer³

et al. 2022

IJACSA

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The present study evaluates the prediction performance of the multi-machine learning models (MLMs) on high-volatile financial markets data sets since 2007 to 2020. The linear and nonlinear empirical data sets are comprised on stock price returns of Karachi stock exchange (KSE) 100-Index of Pakistan and currencies exchange rates of Pakistani Rupees (PKR) against five major currencies (USD, Euro, GBP, CHF & JPY). In the present study, the support vector regression (SVR), random forest (RF), and machine learning-linear regression model (ML-LRM) are under-evaluated for comparative prediction performance. Moreover, the findings demonstrated that the SVR comparatively gives optimal prediction performance on group1. Similarly, the RF relatively gives the best prediction performance on group2. The findings of study concludes that the algorithm of RF is most appropriate for nonlinear approximation/evaluation and the algorithm of SVR is most useful for high-frequency time-series data estimation. The present study is contributed by exploring comparative enthusiastic/optimistic machine learning model on multi-nature data sets. This empirical study would be helpful for finance and machine-learning pupils, data analysts and researchers, especially for those who are deploying machine-learning approaches for financial analysis.

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Section: ) Support Vector Regressionmentioning

confidence: 99%

Evaluation Optimal Prediction Performance of MLMs on High-volatile Financial Market Data

Yao¹,

Naveed²,

Answer³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…SVR refers to a regression algorithm that is used for predicting continuous ordered variables and has been considered as a promising approach for handling the problem of function approximation [23]. SVR works on the principles of SVM, developed originally by Vapnik and his colleagues rooted in statistic learning theory [24].…”

Section: Construction Of the Arkbmentioning

confidence: 99%

“…Due to the protruding advantage in handling the problem of function approximation [23], support vector regression (SVR), working on the principles of support vector machine (SVM) [24], constitutes the computation tool for formalization and modeling the relationships between CPs movement database and structure lines (SLs) length variation. The major advantages of SVR includes (1) it is suitable for both linear and non-linear regressions; (2) performs lower computation compared to other regression techniques;…”

Section: Introductionmentioning

confidence: 99%

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Wang¹,

Zeng²,

Tao³

et al. 2021

IJCIS

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Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design.

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“…Introducing relaxation variable j i andĵ i , the formula can be rewritten as follows (Anand et al, 2020):…”

Section: Black Box Model Of Solenoid Valve Based On Support Vector Regressionmentioning

confidence: 99%

Experimental and numerical validation of a proportional solenoid valve based on the data-driven model

Jiang

Ren

et al. 2021

Transactions of the Institute of Measurement and Control

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Solenoid valves are widely used in mechatronics, robotic systems and industrial occasions. An accurate model is very important for the design and control of a solenoid valve. The dynamical model of the solenoid valve is difficult to obtain due to the complexity of the structure and the interaction of multiple physical fields. This paper proposes two kinds of model of solenoid valve: grey box model and black box model, on the basis of experimental data. ARX model is selected as the basic structure of the grey box model. After clustering the data with the fuzzy c-means algorithm, the overall experimental data is divided into several local linear sub-models, and the model coefficients of the local linear model are obtained by partial least square regression. The overall expression of the model is obtained by combining the local sub-models with membership degree. For the black box model, support vector regression algorithm is used to identify. On the basis of selecting the appropriate parameters, we obtain the black box model of solenoid valve based on data. For the above two models, we carry out experimental verification and error analysis, and compare with the traditional modelling method. According to the results, it can be seen that on the basis of the experimental data, using the data-driven method to construct the model has many advantages, avoiding complex physical analysis, and has high accuracy. The model with high precision will be used in the accurate control and observing estimation of the solenoid valve.

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A class of new Support Vector Regression models

Cited by 30 publications

References 22 publications

Evaluation Optimal Prediction Performance of MLMs on High-volatile Financial Market Data

Evaluation Optimal Prediction Performance of MLMs on High-volatile Financial Market Data

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Experimental and numerical validation of a proportional solenoid valve based on the data-driven model

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