{"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/deep-and-confident-prediction-for-time-series","title":"Deep and Confident Prediction for Time Series at Uber","arxiv_id":"1709.01907","date":"2017-09-06","proceeding":null,"authors":["Lingxue Zhu","Nikolay Laptev"],"abstract":"Reliable uncertainty estimation for time series prediction is critical in\nmany fields, including physics, biology, and manufacturing. At Uber,\nprobabilistic time series forecasting is used for robust prediction of number\nof trips during special events, driver incentive allocation, as well as\nreal-time anomaly detection across millions of metrics. Classical time series\nmodels are often used in conjunction with a probabilistic formulation for\nuncertainty estimation. However, such models are hard to tune, scale, and add\nexogenous variables to. Motivated by the recent resurgence of Long Short Term\nMemory networks, we propose a novel end-to-end Bayesian deep model that\nprovides time series prediction along with uncertainty estimation. We provide\ndetailed experiments of the proposed solution on completed trips data, and\nsuccessfully apply it to large-scale time series anomaly detection at Uber.","url_abs":"http://arxiv.org/abs/1709.01907v1","url_pdf":"http://arxiv.org/pdf/1709.01907v1.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":"deep-and-confident-prediction-for-time-series","repo_url":"https://github.com/GiorgioMorales/PredictionIntervals","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"unanswered"}},{"paper_slug":"deep-and-confident-prediction-for-time-series","repo_url":"https://github.com/ManjunathAdi/Seq2Seq_RNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-and-confident-prediction-for-time-series","repo_url":"https://github.com/NISL-MSU/PredictionIntervals","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"unanswered"}},{"paper_slug":"deep-and-confident-prediction-for-time-series","repo_url":"https://github.com/PawaritL/BayesianLSTM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"deep-and-confident-prediction-for-time-series","repo_url":"https://github.com/jsiloto/dengAI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"deep-and-confident-prediction-for-time-series","repo_url":"https://github.com/vincekellner/demandforecasting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"prediction","task_name":"Prediction"},{"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-anomaly-detection","task_name":"Time Series Anomaly Detection"},{"task_slug":"time-series-forecasting","task_name":"Time Series Forecasting"},{"task_slug":"time-series-prediction","task_name":"Time Series Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/time-series-forecasting-on-hurricane","task":"Time Series Forecasting","dataset":"Extreme Events > Natural Disasters > Hurricane","model":"UberNN","rank_in_archive_order":1,"of":2,"metrics":{"RMSE":"0.453"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.01907","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.01907"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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