{"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-predictive-leading-indicators-for","title":"Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks","arxiv_id":"1710.00569","date":"2017-10-02","proceeding":null,"authors":["Magda Gregorova","Alexandros Kalousis","Stephane Marchand-Maillet"],"abstract":"We present a new method for forecasting systems of multiple interrelated time\nseries. The method learns the forecast models together with discovering leading\nindicators from within the system that serve as good predictors improving the\nforecast accuracy and a cluster structure of the predictive tasks around these.\nThe method is based on the classical linear vector autoregressive model (VAR)\nand links the discovery of the leading indicators to inferring sparse graphs of\nGranger causality. We formulate a new constrained optimisation problem to\npromote the desired sparse structures across the models and the sharing of\ninformation amongst the learning tasks in a multi-task manner. We propose an\nalgorithm for solving the problem and document on a battery of synthetic and\nreal-data experiments the advantages of our new method over baseline VAR models\nas well as the state-of-the-art sparse VAR learning methods.","url_abs":"http://arxiv.org/abs/1710.00569v1","url_pdf":"http://arxiv.org/pdf/1710.00569v1.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-predictive-leading-indicators-for","repo_url":"https://bitbucket.org/dmmlgeneva/var-leading-indicators","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}