In this project, we aim to develop a time series analysis framework using modern machine learning techniques. The project focuses on solving the two fundamental tasks in time series analysis.<\/p>\n
Our approach is based on Bayesian LSTM (opens in new tab)<\/span><\/a>. Besides direct forecasting applications, our research also studies time series analysis in the context of non-markovian reinforcement learning.<\/p>\n The most common framework for RL relies heavily on the markovian-observation assumption, that an agent\u2019s next observation of the environment depends only on its current observation and next action. \u00a0But most real-world environments violate this assumption, as when the agent\u2019s observation is a lossy function of the true environment state. We call such environments partially observable<\/em>.\u00a0In farming for example, we can see the plant, but we do not have access to the full state of the plant or the surrounding environment. We are exploring using RNNs for multivariate time series to model the environment and the hidden state.<\/p>\n Open source (opens in new tab)<\/span><\/a>,\u00a0SLS explained (opens in new tab)<\/span><\/a><\/p>\n TBD<\/p>\n","protected":false},"excerpt":{"rendered":" In this project, we aim to develop a time series analysis framework using modern machine learning techniques. The project focuses on solving the two fundamental tasks in time series analysis. 1. Multivariate Demand Forecasting With Uncertainty Estimate Our approach is based on Bayesian LSTM. Besides direct forecasting applications, our research also studies time series analysis […]<\/p>\n","protected":false},"featured_media":471762,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-471720","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/471720","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":5,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/471720\/revisions"}],"predecessor-version":[{"id":473196,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/471720\/revisions\/473196"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/471762"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=471720"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=471720"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=471720"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=471720"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=471720"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}2. Anomaly Detection<\/h2>\n
2.1. Streaming Least Squares Algorithm for Univariate Time Series Anomaly Detection<\/h3>\n
2.2. Multivariate time series analysis for anomaly prediction<\/h3>\n