{"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/encoding-temporal-markov-dynamics-in-graph","title":"Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series","arxiv_id":"1610.07273","date":"2016-10-24","proceeding":null,"authors":["Lu Liu","Zhiguang Wang"],"abstract":"Time series and signals are attracting more attention across statistics,\nmachine learning and pattern recognition as it appears widely in the industry\nespecially in sensor and IoT related research and applications, but few\nadvances has been achieved in effective time series visual analytics and\ninteraction due to its temporal dimensionality and complex dynamics. Inspired\nby recent effort on using network metrics to characterize time series for\nclassification, we present an approach to visualize time series as complex\nnetworks based on the first order Markov process in its temporal ordering. In\ncontrast to the classical bar charts, line plots and other statistics based\ngraph, our approach delivers more intuitive visualization that better preserves\nboth the temporal dependency and frequency structures. It provides a natural\ninverse operation to map the graph back to raw signals, making it possible to\nuse graph statistics to characterize time series for better visual exploration\nand statistical analysis. Our experimental results suggest the effectiveness on\nvarious tasks such as pattern discovery and classification on both synthetic\nand the real time series and sensor data.","url_abs":"http://arxiv.org/abs/1610.07273v4","url_pdf":"http://arxiv.org/pdf/1610.07273v4.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":"encoding-temporal-markov-dynamics-in-graph","repo_url":"https://github.com/michaelhoarau/mtf-deep-dive","is_official":0,"mentioned_in_paper":0,"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}