Improving the Statistical Arbitrage Strategy in Intraday Trading by Combining Extreme Learning Machine and Support Vector Regression with Linear Regression Models

As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.

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Nonlinear models for recognition of concentration of mixture

Lietuvos matematikos rinkinys ◽

10.15388/lmr.2007.24241 ◽

2021 ◽

Vol 47 ◽

Author(s):

Feliksas Ivanauskas ◽

Robertas Paulauskas ◽

Pranas Vaitkus

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector Regression ◽

Extreme Learning Machine ◽

Nonlinear Models ◽

Support Vector ◽

Artificial Neural ◽

Learning Machine

In this paper extreme learning machine and support vector regression are used for biosensors response to mixtures of compounds classification. The results are compared with the results obtained using artificial neural networks and others.

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Hydrodynamic landslide displacement prediction using combined extreme learning machine and random search support vector regression model

European Journal of Environmental and Civil engineering ◽

10.1080/19648189.2020.1754298 ◽

2020 ◽

pp. 1-13

Author(s):

Rubin Wang ◽

Kun Zhang ◽

Wei Wang ◽

Yongdong Meng ◽

Lanlan Yang ◽

...

Keyword(s):

Regression Model ◽

Support Vector Regression ◽

Extreme Learning Machine ◽

Random Search ◽

Support Vector ◽

Support Vector Regression Model ◽

Displacement Prediction ◽

Landslide Displacement Prediction ◽

Learning Machine

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Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

Mathematical Problems in Engineering ◽

10.1155/2021/9978384 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Sunday O. Olatunji ◽

Taoreed O. Owolabi

Keyword(s):

Barium Titanate ◽

Band Gap ◽

Support Vector Regression ◽

Extreme Learning Machine ◽

Energy Gap ◽

Gravitational Search Algorithm ◽

High Rate ◽

Percentage Error ◽

Support Vector ◽

Learning Machine

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.

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