In English

Anomaly Detection and Fault Localization An Automated Process for Advertising Systems

Moa Persson ; Linnea Rudenius
Göteborg : Chalmers tekniska högskola, 2018. 90 s.
[Examensarbete på avancerad nivå]

The aim of this thesis is to develop an automated process of identifying anomalies in time series and narrowing down the possible root causes. The thesis has been divided into three parts; forecasting, anomaly detection and fault localization. During the forecasting part, different time series models commonly used for forecasting were evaluated, and an exponential smoothing state space model was determined as the best fit for the data used in the project. For the anomaly detection part, an anomaly was defined as a significant deviation from a forecasted value, and different methods for determining a significant deviation were explored. For this part, a threshold learning algorithm was determined as the best method for identifying anomalies. The threshold learning algorithm uses input provided by operators and an updating rule for increasing or decreasing the current threshold. During the last part of this thesis, two different fault localization algorithms were implemented, and the results were compared in order to see which found the largest number of correct root causes. The best performing algorithm was a modified version of the Adtributor algorithm [3], where the modifications included making the algorithm recursive and adjusting the criteria used to determine root cause candidates. The results of the forecasting- and anomaly detection part of this thesis were varied. We believe this is due to the limited amount of labelled data available and the different characteristics present in the time series used. The results from the fault localization were, however, very promising but need to be evaluated using a larger test set. Combining these three components, we believe that the automated process has great potential for discovering anomalies and narrowing down the root causes in a real application.

Nyckelord: Time Series, Forecasting, ARIMA, ETS, Anomaly Detection, Threshold Learning, Fault Localization

Publikationen registrerades 2018-12-14. Den ändrades senast 2018-12-14

CPL ID: 256407

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