{"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/temporally-reweighted-chinese-restaurant","title":"Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series","arxiv_id":"1710.06900","date":"2017-10-18","proceeding":null,"authors":["Feras A. Saad","Vikash K. Mansinghka"],"abstract":"This article proposes a Bayesian nonparametric method for forecasting,\nimputation, and clustering in sparsely observed, multivariate time series data.\nThe method is appropriate for jointly modeling hundreds of time series with\nwidely varying, non-stationary dynamics. Given a collection of $N$ time series,\nthe Bayesian model first partitions them into independent clusters using a\nChinese restaurant process prior. Within a cluster, all time series are modeled\njointly using a novel \"temporally-reweighted\" extension of the Chinese\nrestaurant process mixture. Markov chain Monte Carlo techniques are used to\nobtain samples from the posterior distribution, which are then used to form\npredictive inferences. We apply the technique to challenging forecasting and\nimputation tasks using seasonal flu data from the US Center for Disease Control\nand Prevention, demonstrating superior forecasting accuracy and competitive\nimputation accuracy as compared to multiple widely used baselines. We further\nshow that the model discovers interpretable clusters in datasets with hundreds\nof time series, using macroeconomic data from the Gapminder Foundation.","url_abs":"http://arxiv.org/abs/1710.06900v2","url_pdf":"http://arxiv.org/pdf/1710.06900v2.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":"temporally-reweighted-chinese-restaurant","repo_url":"https://github.com/probcomp/trcrpm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"imputation","task_name":"Imputation"},{"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":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}