Multi-scale Dimensionality Reduction with F-Transforms in Time Series Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F23%3AA2402N58" target="_blank" >RIV/61988987:17610/23:A2402N58 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-39774-5_3" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-39774-5_3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-39774-5_3" target="_blank" >10.1007/978-3-031-39774-5_3</a>
Alternative languages
Result language
angličtina
Original language name
Multi-scale Dimensionality Reduction with F-Transforms in Time Series Analysis
Original language description
Our first contribution to this topic is as follows: we show that in the case of large datasets, dimensionality reduction should be divided into several subtasks, determined by the choice of keypoints as centers corresponding to clusters. For specific time series datasets, we connect keypoints to centers that maximize the values of the non-local Laplacians. Moreover, we propose to use the scale space approach and consider a scale-dependent sequence of non-local Laplacians. As a second contribution, we use non-traditional kernels obtained from the theory of F-transforms [11]. This allows to simplify the scaling and selection of keypoints, reduce their number and increase reliability. We also propose a new keypoint descriptor and test it against high volatility financial time series.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10102 - Applied mathematics
Result continuities
Project
<a href="/en/project/GA18-06915S" target="_blank" >GA18-06915S: New approaches to aggregation operators in analysis and processing of data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Intelligent and Fuzzy Systems. INFUS 2023. Lecture Notes in Networks and Systems, vol 758
ISBN
978-3-031-39773-8
ISSN
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e-ISSN
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Number of pages
13
Pages from-to
22-34
Publisher name
Springer Cham
Place of publication
Switzerland
Event location
Istanbul
Event date
Aug 22, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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