{"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/learning-granger-causality-for-hawkes","title":"Learning Granger Causality for Hawkes Processes","arxiv_id":"1602.04511","date":"2016-02-14","proceeding":null,"authors":["Hongteng Xu","Mehrdad Farajtabar","Hongyuan Zha"],"abstract":"Learning Granger causality for general point processes is a very challenging\ntask. In this paper, we propose an effective method, learning Granger\ncausality, for a special but significant type of point processes --- Hawkes\nprocess. We reveal the relationship between Hawkes process's impact function\nand its Granger causality graph. Specifically, our model represents impact\nfunctions using a series of basis functions and recovers the Granger causality\ngraph via group sparsity of the impact functions' coefficients. We propose an\neffective learning algorithm combining a maximum likelihood estimator (MLE)\nwith a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of\nour model allows to incorporate the clustering structure event types into\nlearning framework. We analyze our learning algorithm and propose an adaptive\nprocedure to select basis functions. Experiments on both synthetic and\nreal-world data show that our method can learn the Granger causality graph and\nthe triggering patterns of the Hawkes processes simultaneously.","url_abs":"http://arxiv.org/abs/1602.04511v2","url_pdf":"http://arxiv.org/pdf/1602.04511v2.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":"learning-granger-causality-for-hawkes","repo_url":"https://github.com/gcastle-hub/dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"point-processes","task_name":"Point Processes"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.04511","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}