Non-stationary Signal Analysis: Detrending and Anomaly Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384607" target="_blank" >RIV/68407700:21230/25:00384607 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26230/26:0200640

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-95911-0_4" target="_blank" >https://doi.org/10.1007/978-3-031-95911-0_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-95911-0_4" target="_blank" >10.1007/978-3-031-95911-0_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-stationary Signal Analysis: Detrending and Anomaly Detection

Popis výsledku v původním jazyce

Smoothing signals, finding a trend component, and detecting anomalies in time series are key tasks in fields such as sensor data processing, healthcare, and cyber security. These challenges become particularly complex when working with data characterized by nonlinear trends, noise, and sudden changes. The situation is further complicated by the limited availability of annotated real-world datasets, which hinders the development and evaluation of supervised models. In this paper, we focus on methods for smoothing time series, identifying underlying trends, and isolating anomalies. We propose an approach based on graph neural networks, designed to detect trends in nonstationary time series with abrupt steps. Our methodology is demonstrated in the context of tram traffic detection, utilizing the signal data measured on the bridge by optical fiber sensors. Due to the absence of annotated real-world data, we evaluated our method using the Reverse Quality Estimator based on the annotated synthetic data and unannotated real data. The performance of our approach is then compared with state-of-the-art unsupervised methods.

Název v anglickém jazyce

Non-stationary Signal Analysis: Detrending and Anomaly Detection

Popis výsledku anglicky

Smoothing signals, finding a trend component, and detecting anomalies in time series are key tasks in fields such as sensor data processing, healthcare, and cyber security. These challenges become particularly complex when working with data characterized by nonlinear trends, noise, and sudden changes. The situation is further complicated by the limited availability of annotated real-world datasets, which hinders the development and evaluation of supervised models. In this paper, we focus on methods for smoothing time series, identifying underlying trends, and isolating anomalies. We propose an approach based on graph neural networks, designed to detect trends in nonstationary time series with abrupt steps. Our methodology is demonstrated in the context of tram traffic detection, utilizing the signal data measured on the bridge by optical fiber sensors. Due to the absence of annotated real-world data, we evaluated our method using the Reverse Quality Estimator based on the annotated synthetic data and unannotated real data. The performance of our approach is then compared with state-of-the-art unsupervised methods.

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

<a href="/cs/project/FW10010028" target="_blank" >FW10010028: Smart inovace monitoringu napětí v progresivních geotechnických aplikacích fotonickými systémy</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Image Analysis

ISBN

978-3-031-95910-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

45-59

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Reykjavik

Datum konání akce

23. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001553875500004