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Mining and Forecasting of Big Time-series Data

Summary: Concise tutorial on mining and forecasting big time-series, covering similarity search, pattern discovery, linear/nonlinear modeling, forecasting, and tensor analysis. Emphasizes automatic mining (no parameter tuning) with intuition and practical case studies for scalable data management. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5025
Venue
SIGMOD
Year
2015
Pagerank
5.1643809e-05
Overall Rank
10,069 | 30.92%
DOI
10.1145/2723372.2731081

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{sakurai_sigmod15,
        title = {{Mining and Forecasting of Big Time-series Data}},
        author = {Sakurai, Yasushi and Matsubara, Yasuko and Faloutsos, Christos},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2731081},
        url = {https://dl.acm.org/doi/10.1145/2723372.2731081},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,923 Improving Time Series Data Compression in Apache IoTDB 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

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

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