Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Reproducing Kernel Hilbert Spaces With Odd Kernels in Price Prediction

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2012

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Transactions on Neural Networks and Learning Systems

ISSN

2162-237X

e-ISSN

—

Svazek periodika

23

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

1564-1573

Kód UT WoS článku

000308966100005

EID výsledku v databázi Scopus

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