{"id":31665,"date":"2023-02-20T06:00:00","date_gmt":"2023-02-20T14:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=31665"},"modified":"2023-06-23T12:38:08","modified_gmt":"2023-06-23T19:38:08","slug":"research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/","title":{"rendered":"Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"alignright size-full is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/02\/MIT_Data_to_AI_logo.png\" alt=\"\" class=\"wp-image-31666\" width=\"270\" height=\"135\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/02\/MIT_Data_to_AI_logo.png 346w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/02\/MIT_Data_to_AI_logo-300x150.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/02\/MIT_Data_to_AI_logo-150x75.png 150w\" sizes=\"(max-width: 270px) 100vw, 270px\" \/><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/kalyan.lids.mit.edu\/\" target=\"_blank\" rel=\"noreferrer noopener\">Kalyan Veeramachaneni<\/a>&nbsp;and his team at the&nbsp;<a href=\"https:\/\/dai.lids.mit.edu\/\" target=\"_blank\" rel=\"noreferrer noopener\">MIT Data-to-AI (DAI) Lab<\/a>&nbsp;have developed the first generative model, the AutoEncoder with Regression (AER) for time series anomaly detection, that combines both reconstruction-based and prediction-based models. They\u2019ve been building it for three years\u2014AER has been learning and extracting intelligence for signals and has reached maturity to outperform the market\u2019s leading models significantly:<\/p>\n\n\n\n<p>The percentage increase of AER (f1=0.7384) based on&nbsp;<strong>version 0.4.1<\/strong>:<\/p>\n\n\n\n<ul>\n<li>194.41% better than Azure Anomaly Detector (f1=0.2508)<\/li>\n\n\n\n<li>95.96% better than IBM GANF (f1=0.3768)<\/li>\n<\/ul>\n\n\n\n<p><em>(Here\u2019s the paper, published at the end of December in IEEE BigData 2022 for your reference:&nbsp;<\/em><a href=\"https:\/\/arxiv.org\/pdf\/2212.13558.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><em>https:\/\/arxiv.org\/pdf\/2212.13558.pdf<\/em><\/a><em>)&nbsp;<\/em><\/p>\n\n\n\n<p><a href=\"https:\/\/github.com\/sintel-dev\/Orion\/tree\/master\/orion\/pipelines\/verified\" target=\"_blank\" rel=\"noreferrer noopener\">AER<\/a>&nbsp;is available publicly within&nbsp;<a href=\"https:\/\/sintel.dev\/Orion\/\" target=\"_blank\" rel=\"noreferrer noopener\">Orion<\/a>, an open source machine learning library for unsupervised time series anomaly detection, that is part of the MIT DAI Lab\u2019s signal intelligence project (<a href=\"https:\/\/sintel.dev\/\" target=\"_blank\" rel=\"noreferrer noopener\">Sintel<\/a>) to analyze large-scale time series data, develop advanced analytics human-in-the-loop workflows, and translate these into actionable insights that predict and prevent unexpected and critical issues.<\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/inside-bigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;<a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on LinkedIn:&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/insidebigdata\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on Facebook:&nbsp;<a href=\"https:\/\/www.facebook.com\/insideBIGDATANOW\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Kalyan Veeramachaneni and his team at the MIT Data-to-AI (DAI) Lab have developed the first generative model, the AutoEncoder with Regression (AER) for time series anomaly detection, that combines both reconstruction-based and prediction-based models. They\u2019ve been building it for three years\u2014AER has been learning and extracting intelligence for signals and has reached maturity to outperform the market\u2019s leading models significantly.<\/p>\n","protected":false},"author":37,"featured_media":31666,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,180,67,268,56,84,1303,1],"tags":[437,718,1262,710,96],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Kalyan Veeramachaneni and his team at the MIT Data-to-AI (DAI) Lab have developed the first generative model, the AutoEncoder with Regression (AER) for time series anomaly detection, that combines both reconstruction-based and prediction-based models. They\u2019ve been building it for three years\u2014AER has been learning and extracting intelligence for signals and has reached maturity to outperform the market\u2019s leading models significantly.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2023-02-20T14:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-23T19:38:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/02\/MIT_Data_to_AI_logo.png\" \/>\n\t<meta property=\"og:image:width\" content=\"346\" \/>\n\t<meta property=\"og:image:height\" content=\"173\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/\",\"url\":\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/\",\"name\":\"Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2023-02-20T14:00:00+00:00\",\"dateModified\":\"2023-06-23T19:38:08+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\",\"name\":\"Daniel Gutierrez\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"caption\":\"Daniel Gutierrez\"},\"description\":\"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2023\/02\/20\/research-highlights-mit-develops-first-generative-model-for-anomaly-detection-that-combines-both-reconstruction-based-and-prediction-based-models\/","og_locale":"en_US","og_type":"article","og_title":"Research Highlights: MIT Develops First Generative Model for Anomaly Detection that Combines both Reconstruction-based and Prediction-based Models - insideBIGDATA","og_description":"Kalyan Veeramachaneni and his team at the MIT Data-to-AI (DAI) Lab have developed the first generative model, the AutoEncoder with Regression (AER) for time series anomaly detection, that combines both reconstruction-based and prediction-based models. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/02\/MIT_Data_to_AI_logo.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-8eJ","jetpack-related-posts":[{"id":31009,"url":"https:\/\/insidebigdata.com\/2022\/12\/02\/research-highlights-rr-metric-guided-adversarial-sentence-generation\/","url_meta":{"origin":31665,"position":0},"title":"Research Highlights: R&#038;R: Metric-guided Adversarial Sentence Generation","date":"December 2, 2022","format":false,"excerpt":"Large language models are a hot topic in AI research right now. 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