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It also has the ability to describe space and time and is widely used in many fields such as system state anomaly detection. However, multi-dimensional time series have problems such as dimensional explosion and data sparseness, as well as complex pattern features such as periods and trends. Such characteristics lead to rule-based anomaly detection methods suffer from poor detection effects. In the big data scenario, deep learning method begins to be applied to anomaly detection tasks for multi-dimensional time series due to its wide coverage and strong learning ability. This work first summarizes the definitions of anomaly detection for multi-dimensional time series and the challenges it faces. Related methods are sorted out, and then the deep learning-based method is emphasized. The existing work and its advantages and disadvantages are summarized. Finally, the shortcomings of the existing algorithms are clarified and the future research direction is explored.<\/jats:p>","DOI":"10.1007\/978-981-16-9229-1_5","type":"book-chapter","created":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T12:03:56Z","timestamp":1642766636000},"page":"71-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep Learning Based Anomaly Detection for Muti-dimensional Time Series: A Survey"],"prefix":"10.1007","author":[{"given":"Zhipeng","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueqiang","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoqi","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"issue":"3","key":"5_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. 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