Machine learning for time series anomaly detection
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/123129 |
| _version_ | 1811092804824203264 |
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| author | Tinawi, Ihssan. |
| author2 | Kalyan Veeramachaneni. |
| author_facet | Kalyan Veeramachaneni. Tinawi, Ihssan. |
| author_sort | Tinawi, Ihssan. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T15:27:14Z |
| format | Thesis |
| id | mit-1721.1/123129 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:27:14Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1231292019-12-05T18:05:04Z Machine learning for time series anomaly detection Tinawi, Ihssan. Kalyan Veeramachaneni. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 55). 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. by Ihssan Tinawi. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-12-05T18:05:02Z 2019-12-05T18:05:02Z 2019 2019 Thesis https://hdl.handle.net/1721.1/123129 1128282917 eng 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. http://dspace.mit.edu/handle/1721.1/7582 55 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Tinawi, Ihssan. Machine learning for time series anomaly detection |
| title | Machine learning for time series anomaly detection |
| title_full | Machine learning for time series anomaly detection |
| title_fullStr | Machine learning for time series anomaly detection |
| title_full_unstemmed | Machine learning for time series anomaly detection |
| title_short | Machine learning for time series anomaly detection |
| title_sort | machine learning for time series anomaly detection |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/123129 |
| work_keys_str_mv | AT tinawiihssan machinelearningfortimeseriesanomalydetection |