Searching the longest common subsequences in distorted data

Kód výsledku v IS VaVaI

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Nalezeny alternativní kódy

RIV/61989100:27350/15:86094721 RIV/61989100:27740/15:86094721

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Searching the longest common subsequences in distorted data

Popis výsledku v původním jazyce

Many models and artificial intelligence methods work with the inputs in the form of time series. Success of many of them strongly depends on ability to quickly and precisely compare two time series or search the mutual parts. Such ability is especially crucial while recognizing characteristic patterns, indexing, prediction or compression. There are many algorithms able to handle that, however, many of them fail while processing distorted data. Unfortunately, the distortion is natural for many types of data collections, e.g. for measurements of natural phenomena such as precipitations, river discharge volume etc. This paper discusses the possibilities of searching such common subsequences in time series and presents a new approach for searching the longest common subsequences in distorted data. This approach is based on modified the dynamic time warping algorithm, which allows the effective processing distorted time series data.

Název v anglickém jazyce

Searching the longest common subsequences in distorted data

Popis výsledku anglicky

Many models and artificial intelligence methods work with the inputs in the form of time series. Success of many of them strongly depends on ability to quickly and precisely compare two time series or search the mutual parts. Such ability is especially crucial while recognizing characteristic patterns, indexing, prediction or compression. There are many algorithms able to handle that, however, many of them fail while processing distorted data. Unfortunately, the distortion is natural for many types of data collections, e.g. for measurements of natural phenomena such as precipitations, river discharge volume etc. This paper discusses the possibilities of searching such common subsequences in time series and presents a new approach for searching the longest common subsequences in distorted data. This approach is based on modified the dynamic time warping algorithm, which allows the effective processing distorted time series data.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

27th European Modeling and Simulation Symposium, EMSS 2015

ISBN

978-88-97999-48-5

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

84-92

Název nakladatele

Dime University of Genoa

Místo vydání

Genova

Místo konání akce

Bergeggi

Datum konání akce

21. 9. 2015

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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