Anomaly detection for short time series data in waste management

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU143627" target="_blank" >RIV/00216305:26210/21:PU143627 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Anomaly detection for short time series data in waste management

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Anomaly detection for short time series data in waste management

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10102 - Applied mathematics

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)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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