Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00197138" target="_blank" >RIV/68407700:21230/12:00197138 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06253266&tag=1" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=06253266&tag=1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNNLS.2012.2207739" target="_blank" >10.1109/TNNLS.2012.2207739</a>
Alternative languages
Result language
angličtina
Original language name
Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction
Original language description
For time series of futures contract prices, the expected price change is modeled conditional on past price changes. The proposed model takes the form of regression in a reproducing kernel Hilbert space with the constraint that the regression function must be odd. It is shown how the resulting constrained optimization problem can be reduced to an unconstrained one through appropriate modification of the kernel. In particular, it is shown how odd, even, and other simile kernels emerge naturally as the reproducing kernels of Hilbert subspaces induced by respective symmetry constraints. To test the validity and practical usefulness of the oddness assumption, experiments are run with large real-world datasets on four futures contracts, and it is demonstrated that using odd kernels results in a higher predictive accuracy and a reduced tendency to overfit.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
e-ISSN
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Volume of the periodical
23
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
1564-1573
UT code for WoS article
000308966100005
EID of the result in the Scopus database
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