From Big Time Series Forecasting to Foundation Models for Structured Data
Summary: Tutorial surveys foundation models for time-series and tabular data, covering tokenization, zero-shot/in-context inference, synthetic priors, and relational pretraining. It highlights convergence and native database integration for forecasting, OLAP, autoscaling, and capacity planning. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Xiyuan Zhang (Amazon)
- 2. Abdul Fatir Ansari (Amazon)
- 3. Christos Faloutsos (Amazon; Carnegie Mellon University)
- 4. George Karypis (NTT Data AIVista; University of Minnesota)
- 5. Yuyang Wang (Amazon)
BibTeX Citation
@article{zhang_vldb26,
title = {{From Big Time Series Forecasting to Foundation Models for Structured Data}},
author = {Zhang, Xiyuan and Ansari, Abdul Fatir and Faloutsos, Christos and Karypis, George and Wang, Yuyang},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4939--4943},
doi = {10.14778/3827998.3828154},
url = {https://doi.org/10.14778/3827998.3828154},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,520 | Classical and Contemporary Approaches to Big Time Series Forecasting | 2019 | SIGMOD | 6.5628257e-05 |
| 5,266 | Forecasting Big Time Series: Old and New | 2018 | VLDB | 6.2023166e-05 |
| 5,304 | Probabilistic Demand Forecasting at Scale | 2017 | VLDB | 6.1873575e-05 |
| 11,607 | A Flexible Forecasting Stack | 2024 | VLDB | 4.9793485e-05 |
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