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**Harvard**

Samuelsson, M. (2016) *Detecting Anomalies In Time Series Data*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

Samuelsson2016,

author={Samuelsson, Moa},

title={Detecting Anomalies In Time Series Data},

abstract={Anomaly detection has shown to be a valuable tool in a variety of application domains,
e.g. detecting credit card fraud, network intrusion and sensor malfunction.
This thesis provides an anomaly detection algorithm as a monitoring aid applied
to time series data from the pulp and paper industry, developed for the company
Eurocon MOPSsys AB. The algorithm is designed to be generally applicable to the
targeted time series by providing methods for adapting parameters to the input data.
The anomaly detection algorithm runs in an unsupervised setting using a statistical
approach for detection. The algorithm works by fitting a statistical model to a
training set of a given size and computing control limits for extracted features of the
data. An anomaly is said to be found if a feature falls outside of its limits that are
constantly updated to adapt to the current data. The thesis also gives an algorithm
that detects changes in the trend of the time series by investigating residuals of
linear fits to calculated trends of the data. The time complexities of the algorithms
are linear in training size which make them suitable to run in an online environment.
The algorithm was evaluated using time series data provided by MOPSsys consisting
of both laboratory and sensor values. As an aid for the evaluation, the time series
were inspected visually to manually label deviating patterns. The anomaly detection
algorithm is shown to be able to find these deviating patterns. However, it could not
be determined whether these patterns are anomalies with respect to the underlying
process as no labelled test data was available. Changes in the trend were also found
to be in agreement with the beforehand expected outcome.
The developed algorithms show promising results but need labelled test data to give
a more accurate evaluation of its performance.},

publisher={Institutionen för matematiska vetenskaper, Chalmers tekniska högskola},

place={Göteborg},

year={2016},

note={108},

}

** RefWorks **

RT Generic

SR Electronic

ID 242944

A1 Samuelsson, Moa

T1 Detecting Anomalies In Time Series Data

YR 2016

AB Anomaly detection has shown to be a valuable tool in a variety of application domains,
e.g. detecting credit card fraud, network intrusion and sensor malfunction.
This thesis provides an anomaly detection algorithm as a monitoring aid applied
to time series data from the pulp and paper industry, developed for the company
Eurocon MOPSsys AB. The algorithm is designed to be generally applicable to the
targeted time series by providing methods for adapting parameters to the input data.
The anomaly detection algorithm runs in an unsupervised setting using a statistical
approach for detection. The algorithm works by fitting a statistical model to a
training set of a given size and computing control limits for extracted features of the
data. An anomaly is said to be found if a feature falls outside of its limits that are
constantly updated to adapt to the current data. The thesis also gives an algorithm
that detects changes in the trend of the time series by investigating residuals of
linear fits to calculated trends of the data. The time complexities of the algorithms
are linear in training size which make them suitable to run in an online environment.
The algorithm was evaluated using time series data provided by MOPSsys consisting
of both laboratory and sensor values. As an aid for the evaluation, the time series
were inspected visually to manually label deviating patterns. The anomaly detection
algorithm is shown to be able to find these deviating patterns. However, it could not
be determined whether these patterns are anomalies with respect to the underlying
process as no labelled test data was available. Changes in the trend were also found
to be in agreement with the beforehand expected outcome.
The developed algorithms show promising results but need labelled test data to give
a more accurate evaluation of its performance.

PB Institutionen för matematiska vetenskaper, Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/242944/242944.pdf

OL 30