mlsanityData to 7 Oct 2026

Deep and Confident Prediction for Time Series at Uber

arXiv 1709.01907 ↗First posted 6 Sep 2017stat.MLcited by 50

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Abstract (arXiv)

Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conjunction with a probabilistic formulation for uncertainty estimation. However, such models are hard to tune, scale, and add exogenous variables to. Motivated by the recent resurgence of Long Short Term Memory networks, we propose a novel end-to-end Bayesian deep model that provides time series prediction along with uncertainty estimation. We provide detailed experiments of the proposed solution on completed trips data, and successfully apply it to large-scale time series anomaly detection at Uber.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2017.

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Citing papers per year: 2020: 1; 2021: 24; 2022: 12; 2023: 6; 2024: 4; 2025: 3.

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  1. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  2. 2021On Hallucination and Predictive Uncertainty in Conditional Language Generationcited by 176
  3. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  4. 2025This Time is Different: An Observability Perspective on Time Series Foundation Modelscited by 57
  5. 2021Quantifying Uncertainty in Deep Spatiotemporal Forecastingcited by 48
  6. 2023Conformal Prediction for Time Series with Modern Hopfield Networkscited by 40
  7. 2021Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Seriescited by 24
  8. 2021A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applicationscited by 24
  9. 2022Uncertainty Quantification for Traffic Forecasting: A Unified Approachcited by 22
  10. 2021Probabilistic Time Series Forecasting with Implicit Quantile Networkscited by 17
  11. 2021CAMul: Calibrated and Accurate Multi-view Time-Series Forecastingcited by 17
  12. 2022TiSAT: Time Series Anomaly Transformercited by 15
38 more
  1. 2022Uncertainty estimation of pedestrian future trajectory using Bayesian approximationcited by 12
  2. 2021DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecastingcited by 11
  3. 2021Deep Time Series Forecasting with Shape and Temporal Criteriacited by 10
  4. 2022Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecastingcited by 10
  5. 2021Deep Bayesian Active Learning for Accelerating Stochastic Simulationcited by 7
  6. 2021Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learningcited by 7
  7. 2024Uncertainty-boosted Robust Video Activity Anticipationcited by 7
  8. 2024DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimationcited by 6
  9. 2020SAFARI: Safe and Active Robot Imitation Learning with Imaginationcited by 5
  10. 2022Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generationcited by 5
  11. 2023A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction modelscited by 5
  12. 2021Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forumscited by 4
  13. 2022A Survey on Dropout Methods and Experimental Verification in Recommendationcited by 4
  14. 2024Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actionscited by 4
  15. 2021Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Seriescited by 3
  16. 2021Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecastingcited by 3
  17. 2022AA-Forecast: Anomaly-Aware Forecast for Extreme Eventscited by 3
  18. 2024Leveraging World Events to Predict E-Commerce Consumer Demand under Anomalycited by 3
  19. 2021A windowed correlation based feature selection method to improve time series prediction of dengue fever casescited by 2
  20. 2023Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trustcited by 2
  21. 2021Accurate and Reliable Forecasting using Stochastic Differential Equationscited by 1
  22. 2021Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networkscited by 1
  23. 2021Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmarkcited by 1
  24. 2022Surrogate Ensemble Forecasting for Dynamic Climate Impact Modelscited by 1
  25. 2025STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Modelscited by 1
  26. 2021Recoding latent sentence representations -- Dynamic gradient-based activation modification in RNNscited by 0
  27. 2021Assessing the Impact: Does an Improvement to a Revenue Management System Lead to an Improved Revenue?cited by 0
  28. 2021Uncertainty Surrogates for Deep Learningcited by 0
  29. 2021Approximating the Manifold Structure of Attributed Incentive Salience from Large Scale Behavioural Data. A Representation Learning Approach Based on Artificial Neural Networkscited by 0
  30. 2021Harnessing value from data science in business: ensuring explainability and fairness of solutionscited by 0
  31. 2021Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem modelcited by 0
  32. 2022Robust Nonparametric Distribution Forecast with Backtest-based Bootstrap and Adaptive Residual Selectioncited by 0
  33. 2022FRANS: Automatic Feature Extraction for Time Series Forecastingcited by 0
  34. 2022Online Learning Models for Vehicle Usage Prediction During COVID-19cited by 0
  35. 2023Lessons Learned Applying Deep Learning Approaches to Forecasting Complex Seasonal Behaviorcited by 0
  36. 2023An evaluation of time series forecasting models on water consumption data: A case study of Greececited by 0
  37. 2023Last layer state space model for representation learning and uncertainty quantificationcited by 0
  38. 2025An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Predictioncited by 0