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http://scholar.google.com/citations?user=sDf3Jv0AAAAJ&hl=en
Their combined citations are counted only for the first article. ... VV Gavrishchaka, GI Ganguli, WA Scales, SP Slinker, CC Chaston, ... Physical review letters 85 (20), 4285, 2000. 82: ... Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting ...
http://aqscs.com/index-1_team.html
valeriy V. Gavrishchaka, phd Google Scholar Profile Valeriy Gavrishchaka received his MS and PhD degrees in computational and theoretical physics from Moscow Institute of Physics and Technology (Russian Federation) and from West Virginia University (USA) in 1989 and 1996, respectively.
https://www.researchgate.net/profile/Valeriy_Gavrishchaka
Valeriy V. Gavrishchaka Supriya Ganguli Neural networks (NN) and support vector machines (SVM) have been used to model high-latitude geomagnetic substorm activity characterized by auroral ...
https://dblp.org/pers/g/Gavrishchaka:Valeriy_V=
Valeriy V. Gavrishchaka, Mark E. Koepke, Olga N. Ulyanova: Boosting-based discovery of multi-component physiological indicators: applications to express diagnostics and …
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2001JA900118
Dec 01, 2001 · The support vector machine (SVM) has been used to model solar wind‐driven geomagnetic substorm activity characterized by the auroral electrojet (AE) index.The focus of the present study, which is the first application of the SVM to space physics problems, is reliable prediction of large‐amplitude substorm events from solar wind and interplanetary magnetic field data.Cited by: 34
https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2001JA900118
Support vector machine as an efficient tool for high-dimensional data processing: Application to substorm forecasting Valeriy V. Gavrishchaka and Supriya B. Ganguli Science Applications International Corporation, McLean, Virginia, USA Abstract. The support vector machine (SVM) has been used to model solar wind-drivenCited by: 34
https://www.deepdyve.com/lp/springer-journals/support-vector-machine-as-an-efficient-framework-for-stock-market-0IDPzktYBj
Feb 16, 2006 · Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting Support Vector Machine as an Efficient Framework for Stock Market Volatility Forecasting Gavrishchaka, Valeriy; Banerjee, Supriya 2006-02-16 00:00:00 Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified.
https://link.springer.com/article/10.1007%2Fs10287-005-0005-5
Feb 16, 2006 · Abstract. Advantages and limitations of the existing models for practical forecasting of stock market volatility have been identified. Support vector machine (SVM) have been proposed as a complimentary volatility model that is capable to extract information from …Cited by: 72
https://www.hindawi.com/journals/mpe/2016/4907654/
This paper investigates the value of designing a new kernel of support vector regression for the application of forecasting high-frequency stock returns. Under the assumption that each return is an event that triggers momentum and reversal periodically, we decompose each future return into a collection of decaying cosine waves that are functions of past returns. Under realistic assumptions, we ...Cited by: 4
http://aqscs.com/Valeriy_Gavrishchaka_CV.pdf
large financial company: design, development, and support of the analytic modules for a new generation of credit-risk management system. ! 5+ years of unique interdisciplinary experience in a leading consulting company conducting cutting edge research in complex system modeling, applied machine learning and artificial
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