{"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/probabilistic-forecasting-of-sensory-data","title":"Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN","arxiv_id":"1903.12549","date":"2019-03-29","proceeding":null,"authors":["Alireza Koochali","Peter Schichtel","Sheraz Ahmed","Andreas Dengel"],"abstract":"Time series forecasting is one of the challenging problems for humankind.\nTraditional forecasting methods using mean regression models have severe\nshortcomings in reflecting real-world fluctuations. While new probabilistic\nmethods rush to rescue, they fight with technical difficulties like quantile\ncrossing or selecting a prior distribution. To meld the different strengths of\nthese fields while avoiding their weaknesses as well as to push the boundary of\nthe state-of-the-art, we introduce ForGAN - one step ahead probabilistic\nforecasting with generative adversarial networks. ForGAN utilizes the power of\nthe conditional generative adversarial network to learn the data generating\ndistribution and compute probabilistic forecasts from it. We argue how to\nevaluate ForGAN in opposition to regression methods. To investigate\nprobabilistic forecasting of ForGAN, we create a new dataset and demonstrate\nour method abilities on it. This dataset will be made publicly available for\ncomparison. Furthermore, we test ForGAN on two publicly available datasets,\nnamely Mackey-Glass dataset and Internet traffic dataset (A5M) where the\nimpressive performance of ForGAN demonstrate its high capability in forecasting\nfuture values.","url_abs":"http://arxiv.org/abs/1903.12549v1","url_pdf":"http://arxiv.org/pdf/1903.12549v1.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":"probabilistic-forecasting-of-sensory-data","repo_url":"https://git.opendfki.de/koochali/forgan","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"probabilistic-time-series-forecasting","task_name":"Probabilistic Time Series Forecasting"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"univariate-time-series-forecasting","task_name":"Univariate Time Series Forecasting"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[{"slug":"lorenz-dataset-1","name":"Lorenz Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/probabilistic-time-series-forecasting-on-3","task":"Probabilistic Time Series Forecasting","dataset":"Internet Traffic dataset (A5M)","model":"ForGAN","rank_in_archive_order":1,"of":1,"metrics":{"CRPS":"6.84e7","KLD":"2.84e-11"},"uses_additional_data":false},{"leaderboard":"/sota/probabilistic-time-series-forecasting-on","task":"Probabilistic Time Series Forecasting","dataset":"Lorenz dataset","model":"ForGAN","rank_in_archive_order":1,"of":1,"metrics":{"CRPS":"1.511","KLD":"1.67e-2"},"uses_additional_data":false},{"leaderboard":"/sota/probabilistic-time-series-forecasting-on-1","task":"Probabilistic Time Series Forecasting","dataset":"Mackey-Glass dataset","model":"ForGAN","rank_in_archive_order":1,"of":1,"metrics":{"CRPS":"1.91e-4","KLD":"3.18e-3"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}