A Flexible Forecasting Stack
Summary: Modular forecasting stack unifying deep and classical methods, automating model selection, and scaling to non‑stationary, many‑series workloads via GluonTS/AutoGluon on SageMaker. Basis for AWS Forecast/Canvas; shares predictive and provisioning lessons from DynamoDB/Redshift/Athena. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Tim Januschowski (Databricks)
- 2. Yuyang Wang (Amazon)
- 3. Jan Gasthaus (Amazon)
- 4. Syama Rangapuram (Amazon)
- 5. Caner Türkmen (Amazon)
- 6. Jasper Zschiegner (Amazon)
- 7. Lorenzo Stella (Amazon)
- 8. Michael Bohlke-Schneider (Amazon)
- 9. Danielle Maddix (Amazon)
- 10. Konstantinos Benidis (Amazon)
- 11. Alexander Alexandrov (Materialize)
- 12. Christos Faloutsos (Amazon; Carnegie Mellon University)
- 13. Sebastian Schelter (Berlin Institute for the Foundations of Learning and Data; Technical University of Berlin)
BibTeX Citation
@article{januschowski_vldb24,
title = {{A Flexible Forecasting Stack}},
author = {Januschowski, Tim and Wang, Yuyang and Gasthaus, Jan and Rangapuram, Syama and Türkmen, Caner and Zschiegner, Jasper and Stella, Lorenzo and Bohlke-Schneider, Michael and Maddix, Danielle and Benidis, Konstantinos and Alexandrov, Alexander and Faloutsos, Christos and Schelter, Sebastian},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {12},
pages = {3883--3892},
doi = {10.14778/3685800.3685813},
url = {https://doi.org/10.14778/3685800.3685813},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,667 | Amazon DynamoDB: A Seamlessly Scalable Non-relational Datastore | 2012 | SIGMOD | 0.00010066726 |
| 1,937 | Elastic Machine Learning Algorithms in Amazon SageMaker | 2020 | SIGMOD | 9.4524758e-05 |
| 3,585 | The evolution of Amazon Redshift (extended abstract) | 2021 | VLDB | 7.2840127e-05 |
| 5,158 | Forecasting Big Time Series: Old and New | 2018 | VLDB | 6.3404905e-05 |
| 5,182 | Probabilistic Demand Forecasting at Scale | 2017 | VLDB | 6.3287692e-05 |
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