{"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/predictive-business-process-monitoring-with","title":"Predictive Business Process Monitoring with LSTM Neural Networks","arxiv_id":"1612.02130","date":"2016-12-07","proceeding":null,"authors":["Niek Tax","Ilya Verenich","Marcello La Rosa","Marlon Dumas"],"abstract":"Predictive business process monitoring methods exploit logs of completed\ncases of a process in order to make predictions about running cases thereof.\nExisting methods in this space are tailor-made for specific prediction tasks.\nMoreover, their relative accuracy is highly sensitive to the dataset at hand,\nthus requiring users to engage in trial-and-error and tuning when applying them\nin a specific setting. This paper investigates Long Short-Term Memory (LSTM)\nneural networks as an approach to build consistently accurate models for a wide\nrange of predictive process monitoring tasks. First, we show that LSTMs\noutperform existing techniques to predict the next event of a running case and\nits timestamp. Next, we show how to use models for predicting the next task in\norder to predict the full continuation of a running case. Finally, we apply the\nsame approach to predict the remaining time, and show that this approach\noutperforms existing tailor-made methods.","url_abs":"http://arxiv.org/abs/1612.02130v2","url_pdf":"http://arxiv.org/pdf/1612.02130v2.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":"predictive-business-process-monitoring-with","repo_url":"https://github.com/fazaki/cycle_prediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"predictive-business-process-monitoring-with","repo_url":"https://github.com/fazaki/time-to-event","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"predictive-business-process-monitoring-with","repo_url":"https://github.com/fazaki/time_to_event","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"predictive-business-process-monitoring-with","repo_url":"https://github.com/ishwarvenugopal/gcn-process-mining","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"predictive-business-process-monitoring-with","repo_url":"https://github.com/verenich/ProcessSequencePrediction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"multivariate-time-series-forecasting","task_name":"Multivariate Time Series Forecasting"},{"task_slug":"predictive-process-monitoring","task_name":"Predictive Process Monitoring"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multivariate-time-series-forecasting-on-bpi","task":"Multivariate Time Series Forecasting","dataset":"BPI challenge '12","model":"LSTM","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"0.76"},"uses_additional_data":false},{"leaderboard":"/sota/multivariate-time-series-forecasting-on","task":"Multivariate Time Series Forecasting","dataset":"Helpdesk","model":"LSTM","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"0.7123"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1612.02130","atlas_url":"https://app.syntology.ai/?focus=1612.02130","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}