{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/derivative-delay-embedding-online-modeling-of","title":"Derivative Delay Embedding: Online Modeling of Streaming Time Series","arxiv_id":"1609.07540","date":"2016-09-24","proceeding":null,"authors":["Zhifei Zhang","Yang song","Wei Wang","Hairong Qi"],"abstract":"The staggering amount of streaming time series coming from the real world\ncalls for more efficient and effective online modeling solution. For time\nseries modeling, most existing works make some unrealistic assumptions such as\nthe input data is of fixed length or well aligned, which requires extra effort\non segmentation or normalization of the raw streaming data. Although some\nliterature claim their approaches to be invariant to data length and\nmisalignment, they are too time-consuming to model a streaming time series in\nan online manner. We propose a novel and more practical online modeling and\nclassification scheme, DDE-MGM, which does not make any assumptions on the time\nseries while maintaining high efficiency and state-of-the-art performance. The\nderivative delay embedding (DDE) is developed to incrementally transform time\nseries to the embedding space, where the intrinsic characteristics of data is\npreserved as recursive patterns regardless of the stream length and\nmisalignment. Then, a non-parametric Markov geographic model (MGM) is proposed\nto both model and classify the pattern in an online manner. Experimental\nresults demonstrate the effectiveness and superior classification accuracy of\nthe proposed DDE-MGM in an online setting as compared to the state-of-the-art.","url_abs":"http://arxiv.org/abs/1609.07540v1","url_pdf":"http://arxiv.org/pdf/1609.07540v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"derivative-delay-embedding-online-modeling-of","repo_url":"https://github.com/ZZUTK/Delay_Embedding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}