{"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/forecasting-and-granger-modelling-with-non","title":"Forecasting and Granger Modelling with Non-linear Dynamical Dependencies","arxiv_id":"1706.08811","date":"2017-06-27","proceeding":null,"authors":["Magda Gregorová","Alexandros Kalousis","Stéphane Marchand-Maillet"],"abstract":"Traditional linear methods for forecasting multivariate time series are not\nable to satisfactorily model the non-linear dependencies that may exist in\nnon-Gaussian series. We build on the theory of learning vector-valued functions\nin the reproducing kernel Hilbert space and develop a method for learning\nprediction functions that accommodate such non-linearities. The method not only\nlearns the predictive function but also the matrix-valued kernel underlying the\nfunction search space directly from the data. Our approach is based on learning\nmultiple matrix-valued kernels, each of those composed of a set of input\nkernels and a set of output kernels learned in the cone of positive\nsemi-definite matrices. In addition to superior predictive performance in the\npresence of strong non-linearities, our method also recovers the hidden dynamic\nrelationships between the series and thus is a new alternative to existing\ngraphical Granger techniques.","url_abs":"http://arxiv.org/abs/1706.08811v1","url_pdf":"http://arxiv.org/pdf/1706.08811v1.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":"forecasting-and-granger-modelling-with-non","repo_url":"https://bitbucket.org/dmmlgeneva/nonlinear-granger","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"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}