{"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/lstm-networks-for-data-aware-remaining-time","title":"LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances","arxiv_id":"1711.03822","date":"2017-11-10","proceeding":null,"authors":["Nicolò Navarin","Beatrice Vincenzi","Mirko Polato","Alessandro Sperduti"],"abstract":"Predicting the completion time of business process instances would be a very\nhelpful aid when managing processes under service level agreement constraints.\nThe ability to know in advance the trend of running process instances would\nallow business managers to react in time, in order to prevent delays or\nundesirable situations. However, making such accurate forecasts is not easy:\nmany factors may influence the required time to complete a process instance. In\nthis paper, we propose an approach based on deep Recurrent Neural Networks\n(specifically LSTMs) that is able to exploit arbitrary information associated\nto single events, in order to produce an as-accurate-as-possible prediction of\nthe completion time of running instances. Experiments on real-world datasets\nconfirm the quality of our proposal.","url_abs":"http://arxiv.org/abs/1711.03822v1","url_pdf":"http://arxiv.org/pdf/1711.03822v1.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":"lstm-networks-for-data-aware-remaining-time","repo_url":"https://github.com/nickgentoo/DALSTM_PM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}