Non-stationary Signal Analysis: Detrending and Anomaly Detection

Result code in 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>

Alternative codes found

RIV/00216305:26230/26:0200640

Result on the web

<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>

Result language

angličtina

Original language name

Non-stationary Signal Analysis: Detrending and Anomaly Detection

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/FW10010028" target="_blank" >FW10010028: Smart Innovation of Stress Monitoring in the Progressive Geotechnical Applications Using Fiber Optic System</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

Image Analysis

ISBN

978-3-031-95910-3

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

15

Pages from-to

45-59

Publisher name

Springer, Cham

Place of publication

—

Event location

Reykjavik

Event date

Jun 23, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001553875500004