{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/123129"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/123129","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Machine learning for time series anomaly detection","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Tinawi, Ihssan."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Kalyan Veeramachaneni."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:22:13Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"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."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/123129","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kalyan Veeramachaneni."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Machine learning for time series anomaly detection"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kalyan Veeramachaneni."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. 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