Corpus ID: 3352788

Mining Sub-Interval Relationships In Time Series Data

@article{Agrawal2018MiningSR,
  title={Mining Sub-Interval Relationships In Time Series Data},
  author={Saurabh Agrawal and Saurabh Verma and G. Atluri and A. Karpatne and S. Liess and A. MacDonald and Snigdhansu Chatterjee and Vipin Kumar},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.06095}
}
Time-series data is being increasingly collected and stud- ied in several areas such as neuroscience, climate science, transportation, and social media. Discovery of complex patterns of relationships between individual time-series, using data-driven approaches can improve our understanding of real-world systems. While traditional approaches typically study relationships between two entire time series, many interesting relationships in real-world applications exist in small sub-intervals of time… Expand

References

SHOWING 1-10 OF 23 REFERENCES
Clustering of time series data - a survey
  • T. Liao
  • Computer Science
  • Pattern Recognit.
  • 2005
SpADe: On Shape-based Pattern Detection in Streaming Time Series
Finding Similar Time Series
Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets
A graph-based approach to find teleconnections in climate data
Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins
Discovering Longest-lasting Correlation in Sequence Databases
Exact indexing of dynamic time warping
...
1
2
3
...