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Forecasting High-Dimensional Data

Summary: Forecasting high-dimensional data by forecasting a subset and predicting the rest with correlation models. Evaluates independence- and sample-based models, showing gains when correlations are fully captured; deployed in Yahoo's display system. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4375
Venue
SIGMOD
Year
2010
Pagerank
5.227679e-05
Overall Rank
9,752 | 33.10%
DOI
10.1145/1807167.1807277

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{agarwal_sigmod10,
        title = {{Forecasting High-Dimensional Data}},
        author = {Agarwal, Deepak and Chen, Datong and Lin, Long-ji and Shanmugasundaram, Jayavel and Vee, Erik},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807277},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807277},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
11,736 FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data 2021 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
104 Improved Query Performance with Variant Indexes 1997 SIGMOD 0.00033932213
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