Unsupervised Algorithm for Retrieving Characteristic Patterns from Time-warped Data Collections

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RIV/61989100:27200/12:86084901

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Unsupervised Algorithm for Retrieving Characteristic Patterns from Time-warped Data Collections

Popis výsledku v původním jazyce

This paper discusses possibilities of using the Voting Experts algorithm enhanced by the Dynamic Time Warping (DTW) method for improving performance of Case-Based Reasoning (CBR) methodology used with time-warped data collections. CBR, in general, is the process of solving new problems based on the solutions of similar past problems. Success of this methodology strongly depends on the ability to find similar past situations. Searching these similar situations in data collections with components generated in equidistant time and in finite number of levels is now a trivial task. The problem arises for data collections that are subject to different types of distortions (e.g. measurement of natural phenomena such as precipitations, measured discharge volume etc.). The main goal of this paper is to provide suitable mechanism for retrieving typical patterns from distorted time series and thus improve the usability of CBR.

Název v anglickém jazyce

Unsupervised Algorithm for Retrieving Characteristic Patterns from Time-warped Data Collections

Popis výsledku anglicky

This paper discusses possibilities of using the Voting Experts algorithm enhanced by the Dynamic Time Warping (DTW) method for improving performance of Case-Based Reasoning (CBR) methodology used with time-warped data collections. CBR, in general, is the process of solving new problems based on the solutions of similar past problems. Success of this methodology strongly depends on the ability to find similar past situations. Searching these similar situations in data collections with components generated in equidistant time and in finite number of levels is now a trivial task. The problem arises for data collections that are subject to different types of distortions (e.g. measurement of natural phenomena such as precipitations, measured discharge volume etc.). The main goal of this paper is to provide suitable mechanism for retrieving typical patterns from distorted time series and thus improve the usability of CBR.

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)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

The 11th International Conference on Modeling and Applied Simulation, MAS 2012 : September 19-21 2012, Vienna, Austria

ISBN

978-88-97999-02-7

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

94-99

Název nakladatele

DIME Universita Di Genova

Místo vydání

Genova

Místo konání akce

Vídeň

Datum konání akce

19. 9. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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