Deep and Confident Prediction for Time Series at Uber
Open accessAbstract
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.
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Burez et al. utilized a method called the CUBE method to improve the churn prediction performance.
“Burez, Jonathan, and Dirk Van den Poel.(2009) utilized a method called the CUBE method to improve the churn prediction performance.[95].”
From A Survey on Churn Analysis in Various Business Domains (2020) · cites this paper as [95]
The paper’s introduction (p. 1) and evaluation (pp. 5–7) concern time-series forecasting and anomaly detection at Uber. Searches for Burez, Van den Poel, CUBE, and churn prediction found no passage addressing the attributed claim. (LLM, conf=0.76)
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Cites
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Cited by
Sorted by citations
Recurrent Neural Networks for Time Series Forecasting: Current status and future directions
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.008 · Cited by 1,371 · see citing works
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Benidis, K., Rangapuram, S. S., Flunkert, V., Wang, Y., Maddix, D., Turkmen, C., Gasthaus, J., Bohlke-Schneider, M., Salinas, D., Stella, L., et al. (2023) · ACM Computing Surveys · doi:10.1145/3533382 · Cited by 382 · see citing works
A Survey on Churn Analysis in Various Business Domains
Ahn, J., Hwang, J., Kim, D., Choi, H., & Kang, S. (2020) · IEEE Access · doi:10.1109/access.2020.3042657 · Cited by 108 · see citing works
On hallucination and predictive uncertainty in conditional language generation
Xiao, Y., & Wang, W. Y. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.236 · Cited by 79 · see citing works
- Authors
- Zhu, L., & Laptev, N.
- ORCID
- Zhu, Lingxue
- Venue
- 2017 IEEE International Conference on Data Mining Workshops (ICDMW)
- Publisher
- IEEE
- Published
- 2017-11
- Pages
- 103-110
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W2751802138
- Cited by
- 362
- References
- 4
- Citations by year
- 2026: 7 · 2025: 25 · 2024: 39 · 2023: 40 · 2022: 56 · 2021: 81 · 2020: 65 · 2019: 32 · 2018: 16 · 2017: 1
- FWCI
- 22.14
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE ... International Conference on Data Mining workshops (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- uncertainty estimation, Bayesian deep learning, LSTM, time series anomaly detection, exogenous variables
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