{"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/an-interpretable-and-sparse-neural-network","title":"An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery","arxiv_id":"1711.08160","date":"2017-11-22","proceeding":null,"authors":["Alex Tank","Ian Cover","Nicholas J. Foti","Ali Shojaie","Emily B. Fox"],"abstract":"While most classical approaches to Granger causality detection repose upon\nlinear time series assumptions, many interactions in neuroscience and economics\napplications are nonlinear. We develop an approach to nonlinear Granger\ncausality detection using multilayer perceptrons where the input to the network\nis the past time lags of all series and the output is the future value of a\nsingle series. A sufficient condition for Granger non-causality in this setting\nis that all of the outgoing weights of the input data, the past lags of a\nseries, to the first hidden layer are zero. For estimation, we utilize a group\nlasso penalty to shrink groups of input weights to zero. We also propose a\nhierarchical penalty for simultaneous Granger causality and lag estimation. We\nvalidate our approach on simulated data from both a sparse linear\nautoregressive model and the sparse and nonlinear Lorenz-96 model.","url_abs":"http://arxiv.org/abs/1711.08160v2","url_pdf":"http://arxiv.org/pdf/1711.08160v2.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":"an-interpretable-and-sparse-neural-network","repo_url":"https://github.com/christeefy/Novel-Techniques-for-PTR-FD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}