Learning Entropy: On Shannon vs. Machine-Learning-Based Information in Time Series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F22%3A43906130" target="_blank" >RIV/60076658:12310/22:43906130 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-14343-4_38" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-14343-4_38</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-14343-4_38" target="_blank" >10.1007/978-3-031-14343-4_38</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Entropy: On Shannon vs. Machine-Learning-Based Information in Time Series

Popis výsledku v původním jazyce

The paper discusses the Learning-based information (L) and Learning Entropy (LE) in contrast to classical Shannon probabilistic Information (I) and probabilistic entropy (H). It is shown that L corresponds to the recently introduced Approximate Individual Sample-point Learning Entropy (AISLE). For data series, then, the LE should be defined as the mean value of L that is finally in proper accordance with Shannon&apos;s concept of entropy H. The distinction of L against I is explained by the real-time anomaly detection of individual time series data points (states). First, the principal distinction of the information concept of Ivs.L is demonstrated in respect to data governing law that L considers explicitly (while I does not). Second, it is shown that L has the potential to be applied on much shorter datasets than I because of the learning system being pre-trained and being able to generalize from a smaller dataset. Then, floating window trajectories of the covariance matrix norm, the trajectory of approximate variance fractal dimension, and especially the windowed Shannon Entropy trajectory are compared to LE on multichannel EEG featuring epileptic seizure. The results on real time series show that L, i.e., AISLE, can be a useful counterpart to Shannon entropy allowing us also for more detailed search of anomaly onsets (change points).

Název v anglickém jazyce

Learning Entropy: On Shannon vs. Machine-Learning-Based Information in Time Series

Popis výsledku anglicky

The paper discusses the Learning-based information (L) and Learning Entropy (LE) in contrast to classical Shannon probabilistic Information (I) and probabilistic entropy (H). It is shown that L corresponds to the recently introduced Approximate Individual Sample-point Learning Entropy (AISLE). For data series, then, the LE should be defined as the mean value of L that is finally in proper accordance with Shannon&apos;s concept of entropy H. The distinction of L against I is explained by the real-time anomaly detection of individual time series data points (states). First, the principal distinction of the information concept of Ivs.L is demonstrated in respect to data governing law that L considers explicitly (while I does not). Second, it is shown that L has the potential to be applied on much shorter datasets than I because of the learning system being pre-trained and being able to generalize from a smaller dataset. Then, floating window trajectories of the covariance matrix norm, the trajectory of approximate variance fractal dimension, and especially the windowed Shannon Entropy trajectory are compared to LE on multichannel EEG featuring epileptic seizure. The results on real time series show that L, i.e., AISLE, can be a useful counterpart to Shannon entropy allowing us also for more detailed search of anomaly onsets (change points).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2022 WORKSHOPS

ISBN

978-3-031-14343-4

ISSN

1865-0929

e-ISSN

1865-0937

Počet stran výsledku

14

Strana od-do

402-415

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Vienna Univ Econ &amp; Business

Datum konání akce

22. 8. 2022

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

000870761200038