Paper Info
Title
GRAIL: Efficient Time-Series Representation Learning.
Abstract
The analysis of time series is becoming increasingly prevalent across scientific disciplines and industrial applications. The effectiveness and the scalability of time-series mining techniques critically depend on design choices for three components responsible for (i) representing; (ii) comparing; and (iii) indexing time series. Unfortunately, these components have to date been investigated and developed independently, often resulting in mutually incompatible methods. The lack of a unified approach has hindered progress towards fast and accurate analytics over massive time-series collections. To address this major drawback, we present GRAIL, a generic framework to learn compact time-series representations that preserve the properties of a user-specified comparison function. Given the comparison function, GRAIL (i) extracts landmark time series using clustering; (ii) optimizes necessary parameters; and (iii) exploits approximations for kernel methods to construct representations in linear time and space by expressing each time series as a combination of the landmark time series. We extensively evaluate GRAIL for querying, classification, clustering, sampling, and visualization of time series. For these tasks, methods leveraging GRAIL's representations are significantly faster and at least as accurate as state-of-the-art methods operating over the raw time series. GRAIL shows promise as a new primitive for highly accurate, yet scalable, time-series analysis.
Year
DOI
Venue
2019
10.14778/3342263.3342648
PVLDB
Field
DocType
Volume
Data mining,Visualization,Computer science,Search engine indexing,Artificial intelligence,Time complexity,Analytics,Cluster analysis,Kernel method,Landmark,Machine learning,Scalability
Journal
12
Issue
ISSN
Citations 
11
2150-8097
2
PageRank 
References 
Authors
0.36
0
2
Name
Order
Citations
PageRank
Ioannis Paparrizos110111.59
Michael J. Franklin2174231681.10