Measure of Dependence for Financial Time-Series
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3ALJIWIYRI" target="_blank" >RIV/00216208:11320/23:LJIWIYRI - isvavai.cz</a>
Result on the web
<a href="http://arxiv.org/abs/2311.12129" target="_blank" >http://arxiv.org/abs/2311.12129</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Measure of Dependence for Financial Time-Series
Original language description
"Assessing the predictive power of both data and models holds paramount significance in time-series machine learning applications. Yet, preparing time series data accurately and employing an appropriate measure for predictive power seems to be a non-trivial task. This work involves reviewing and establishing the groundwork for a comprehensive analysis of shaping time-series data and evaluating various measures of dependence. Lastly, we present a method, framework, and a concrete example for selecting and evaluating a suitable measure of dependence."
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů