Detail publikace

Anomaly detection for short time series data in waste management

ROSECKÝ, M. ŠRAMKOVÁ, K. ŠOMPLÁK, R. SMEJKALOVÁ, V.

Originální název

Anomaly detection for short time series data in waste management

Typ

abstrakt

Jazyk

angličtina

Originální abstrakt

Anomaly detection is a very important step in every analysis of real-world data. Presence of the anomalies may strongly affect results of both tested hypotheses and created models. Data analysis is important in waste management to improve effective planning from both short- and long-term perspective. However, in the field of waste management, anomaly detection is rarely done. The goal of our paper is to propose a complex framework for anomaly detection in a big number of short time series. In such a case, it is not possible to use only an expert-based approach due to the time-consuming nature of this process and subjectivity. Proposed framework consists of two steps: 1. outlier detection via outlier test for trend adjusted data, 2. changepoints (trend changepoint, step changepoint) are identified via comparison of linear model parameters. Proposed framework is demonstrated on waste management data from the Czech Republic.

Klíčová slova

Waste management; short time series; anomaly detection; outlier; trend changepoint; step changepoint

Autoři

ROSECKÝ, M.; ŠRAMKOVÁ, K.; ŠOMPLÁK, R.; SMEJKALOVÁ, V.

Vydáno

17. 10. 2021

ISSN

1847-7178

Periodikum

Proceedings of SDEWES Conference on Sustainable Development of Energy, Water and Environment Systems

Stát

Chorvatská republika

BibTex

@misc{BUT176744,
  author="Martin {Rosecký} and Kristína {Šramková} and Radovan {Šomplák} and Veronika {Smejkalová}",
  title="Anomaly detection for short time series data in waste management",
  year="2021",
  journal="Proceedings of SDEWES Conference on Sustainable Development of Energy, Water and Environment Systems",
  issn="1847-7178",
  note="abstract"
}