CESNET TS-Zoo: A Library for Reproducible Analysis of Network Traffic Time Series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F25%3A10133871" target="_blank" >RIV/63839172:_____/25:10133871 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21240/25:00386911

Výsledek na webu

<a href="http://dx.doi.org/10.23919/CNSM67658.2025.11297513" target="_blank" >http://dx.doi.org/10.23919/CNSM67658.2025.11297513</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/CNSM67658.2025.11297513" target="_blank" >10.23919/CNSM67658.2025.11297513</a>

Jazyk výsledku

angličtina

Název v původním jazyce

CESNET TS-Zoo: A Library for Reproducible Analysis of Network Traffic Time Series

Popis výsledku v původním jazyce

Time Series Analysis (TSA) is an essential tool in computer networking, supporting tasks such as traffic forecasting, capacity planning, load balancing, quality of service monitoring, behavior profiling, and anomaly detection. Despite its widespread use, the community was limited by the lack of sufficient datasets. Our recent dataset, CESNET-TimeSeries24, finally fills this gap. However, its substantial size presents significant challenges for practical use in research. Therefore, inspired by the other machine learning communities that often develop supportive tools and benchmarks to accelerate research, we introduced a CESNET TS-Zoo library. It is designed to streamline dataset management, experiment setting, and reproducibility in the TSA of network traffic. TS-Zoo provides a standardized API for accessing the CESNET-TimeSeries24 dataset and includes methods for time series preprocessing, multiple dataset partitioning, and data loading for experiments. Furthermore, the preprocessing steps can be exported and imported, enabling reproducible experiments. Therefore, the TS-Zoo library simplifies TSA experiments and enables reproducibility of TSA research applied in computer networking

Název v anglickém jazyce

CESNET TS-Zoo: A Library for Reproducible Analysis of Network Traffic Time Series

Popis výsledku anglicky

Time Series Analysis (TSA) is an essential tool in computer networking, supporting tasks such as traffic forecasting, capacity planning, load balancing, quality of service monitoring, behavior profiling, and anomaly detection. Despite its widespread use, the community was limited by the lack of sufficient datasets. Our recent dataset, CESNET-TimeSeries24, finally fills this gap. However, its substantial size presents significant challenges for practical use in research. Therefore, inspired by the other machine learning communities that often develop supportive tools and benchmarks to accelerate research, we introduced a CESNET TS-Zoo library. It is designed to streamline dataset management, experiment setting, and reproducibility in the TSA of network traffic. TS-Zoo provides a standardized API for accessing the CESNET-TimeSeries24 dataset and includes methods for time series preprocessing, multiple dataset partitioning, and data loading for experiments. Furthermore, the preprocessing steps can be exported and imported, enabling reproducible experiments. Therefore, the TS-Zoo library simplifies TSA experiments and enables reproducibility of TSA research applied in computer networking

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

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

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2025 21st International Conference on Network and Service Management (CNSM)

ISBN

978-3-903176-75-1

ISSN

2165-963X

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Bologna, Italy

Místo konání akce

Bologna, Italy

Datum konání akce

27. 10. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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