Machine learning for time series anomaly detection

In this thesis, I explored machine learning and other statistical techniques for anomaly detection on time series data obtained from Internet-of-Things sensors. The data, obtained from satellite telemetry signals, were used to train models to forecast a signal based on its historic patterns. When the prediction passed a dynamic error threshold, then that point was flagged as anomalous. I used multiple models such as Long Short-Term Memory (LSTM), autoregression, Multi-Layer Perceptron, and Encoder-Decoder LSTM. I used the "Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding" paper as a basis for my analysis, and was able to beat their performance on anomaly detection by obtaining an F0.5 score of 76%, an improvement over their 69% score.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Tinawi, Ihssan.

Advisor dc:contributor.advisor

• Kalyan Veeramachaneni.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123129

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123129