# Deep and Confident Prediction for Time Series at Uber

arXiv: https://arxiv.org/abs/1709.01907 · HTML: https://mlsanity.com/paper/1709.01907/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2017.

## Built on by (most cited first, 30 of 50)

- [Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2101.12072/) (2021)
- [On Hallucination and Predictive Uncertainty in Conditional Language Generation](https://mlsanity.com/paper/2103.15025/) (2021)
- [Deep Learning for Time Series Anomaly Detection: A Survey](https://mlsanity.com/paper/2211.05244/) (2022)
- [This Time is Different: An Observability Perspective on Time Series Foundation Models](https://mlsanity.com/paper/2505.14766/) (2025)
- [Quantifying Uncertainty in Deep Spatiotemporal Forecasting](https://mlsanity.com/paper/2105.11982/) (2021)
- [Conformal Prediction for Time Series with Modern Hopfield Networks](https://mlsanity.com/paper/2303.12783/) (2023)
- [Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series](https://mlsanity.com/paper/2102.12612/) (2021)
- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Uncertainty Quantification for Traffic Forecasting: A Unified Approach](https://mlsanity.com/paper/2208.05875/) (2022)
- [Probabilistic Time Series Forecasting with Implicit Quantile Networks](https://mlsanity.com/paper/2107.03743/) (2021)
- [CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting](https://mlsanity.com/paper/2109.07438/) (2021)
- [TiSAT: Time Series Anomaly Transformer](https://mlsanity.com/paper/2203.05167/) (2022)
- [Uncertainty estimation of pedestrian future trajectory using Bayesian approximation](https://mlsanity.com/paper/2205.01887/) (2022)
- [DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting](https://mlsanity.com/paper/2102.06684/) (2021)
- [Deep Time Series Forecasting with Shape and Temporal Criteria](https://mlsanity.com/paper/2104.04610/) (2021)
- [Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting](https://mlsanity.com/paper/2204.01618/) (2022)
- [Deep Bayesian Active Learning for Accelerating Stochastic Simulation](https://mlsanity.com/paper/2106.02770/) (2021)
- [Multivariate Anomaly Detection based on Prediction Intervals Constructed using Deep Learning](https://mlsanity.com/paper/2110.03393/) (2021)
- [Uncertainty-boosted Robust Video Activity Anticipation](https://mlsanity.com/paper/2404.18648/) (2024)
- [Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions](https://mlsanity.com/paper/2406.07767/) (2024)
- [DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation](https://mlsanity.com/paper/2408.12809/) (2024)
- [SAFARI: Safe and Active Robot Imitation Learning with Imagination](https://mlsanity.com/paper/2011.09586/) (2020)
- [Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation](https://mlsanity.com/paper/2212.06370/) (2022)
- [A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models](https://mlsanity.com/paper/2307.05946/) (2023)
- [Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums](https://mlsanity.com/paper/2104.12643/) (2021)
- [A Survey on Dropout Methods and Experimental Verification in Recommendation](https://mlsanity.com/paper/2204.02027/) (2022)
- [Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series](https://mlsanity.com/paper/2102.07289/) (2021)
- [Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting](https://mlsanity.com/paper/2112.02622/) (2021)
- [AA-Forecast: Anomaly-Aware Forecast for Extreme Events](https://mlsanity.com/paper/2208.09933/) (2022)
- [Leveraging World Events to Predict E-Commerce Consumer Demand under Anomaly](https://mlsanity.com/paper/2405.13995/) (2024)

## 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.