aalto1 untyped-item.component.html
Semi-automated labeling of time series for anomaly detection
Loading...
URL
Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Authors
Date
Department
Major/Subject
Mcode
SCI3115
Degree programme
Language
en
Pages
42
Series
Abstract
Anomaly detection in Time Series is a widespread topic because there is a huge amount of Time Series data, and anomalies in them indicate possible important events. However, labeled data sets are scarce resource, thus studying and implementing automated or semi-automated labeling algorithms for anomalies in Time Series is a promising research area. This work presents a semi-automated labeling technique for anomaly detection in Time Series. I propose a tool that extracts most prominent parts of a series as a candidates to label as normal or anomalous. Additionally, I integrated this tool into existing Time Series analysis system. The goal is to present to human annotator the most representative parts of the series, but in a reasonable amount, so that human expert can decide which area is of real interest. We can further search for areas similar to those confirmed by human expert and propagate corresponding labels, so that time and effort are minimized.