GRAIL: Efficient Time-Series Representation Learning
Summary: GRAIL introduces a unified time-series representation preserving a user-specified similarity. It expresses each series as a linear combination of landmark series, via clustering and kernel-approximation, enabling linear-time analytics for querying, classification, and clustering. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. John Paparrizos (University of Chicago)
- 2. Michael J. Franklin (University of Chicago)
BibTeX Citation
@article{paparrizos_vldb19,
title = {{GRAIL: Efficient Time-Series Representation Learning}},
author = {Paparrizos, John and Franklin, Michael J.},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {11},
pages = {1762--1777},
doi = {10.14778/3342263.3342648},
url = {https://doi.org/10.14778/3342263.3342648},
year = {2019}
}
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