Time Series: How Unusual Local Behavior Can Be Recognized Using Fuzzy Modeling Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F21%3AA2202DMF" target="_blank" >RIV/61988987:17610/21:A2202DMF - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-45619-1_13" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-45619-1_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-45619-1_13" target="_blank" >10.1007/978-3-030-45619-1_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Time Series: How Unusual Local Behavior Can Be Recognized Using Fuzzy Modeling Methods

Popis výsledku v původním jazyce

In this paper, we address the problem of automatic recognition of structural breaks in time series. The former are unexpected shifts of the course or sudden change of the volatility of time series. Structural breaks can be caused, e.g., by changes in the organization of a company, global or local economic development, global shifts in capital and labor, various kinds of outer influences such as discovery or depletion of natural resources, etc. Structural breaks in time series are usually detected using statistical methods. In this paper, we suggest using special non-statistical techniques of fuzzy modeling. We will employ two classes of them, namely the fuzzy transform and selected methods of Fuzzy Natural Logic. The fuzzy transform enables us to estimate the average slope of time series in an area characterized by a fuzzy set. The slope is then evaluated by evaluative linguistic expressions, which enables us to identify intervals with monotonous behavior and, consequently, identify structural breaks. Our method is simple, transparent, and computationally effective.

Název v anglickém jazyce

Time Series: How Unusual Local Behavior Can Be Recognized Using Fuzzy Modeling Methods

Popis výsledku anglicky

In this paper, we address the problem of automatic recognition of structural breaks in time series. The former are unexpected shifts of the course or sudden change of the volatility of time series. Structural breaks can be caused, e.g., by changes in the organization of a company, global or local economic development, global shifts in capital and labor, various kinds of outer influences such as discovery or depletion of natural resources, etc. Structural breaks in time series are usually detected using statistical methods. In this paper, we suggest using special non-statistical techniques of fuzzy modeling. We will employ two classes of them, namely the fuzzy transform and selected methods of Fuzzy Natural Logic. The fuzzy transform enables us to estimate the average slope of time series in an area characterized by a fuzzy set. The slope is then evaluated by evaluative linguistic expressions, which enables us to identify intervals with monotonous behavior and, consequently, identify structural breaks. Our method is simple, transparent, and computationally effective.

Druh

C - Kapitola v odborné knize

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

<a href="/cs/project/GA18-13951S" target="_blank" >GA18-13951S: Nové přístupy k modelování finančních časových řad pomocí soft-computingu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Statistical and Fuzzy Approaches to Data Processing, with Applications to Econometrics and Other Areas

ISBN

978-3-030-45618-4

Počet stran výsledku

21

Strana od-do

157-177

Počet stran knihy

265

Název nakladatele

Springer

Místo vydání

Cham

Kód UT WoS kapitoly

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