ML Anthology

Data Structures for Detecting Rare Variations in Time Series

Caio Dias Valentim, Eduardo Sany Laber, David Sotelo
ECML-PKDD 2012 pp. 709-724
doi:10.1007/978-3-642-33486-3_45 /ecmlpkdd/2012/valentim2012ecmlpkdd-data/

Abstract

In this paper we study, from both a theoretical and an experimental perspective, algorithms and data structures to process queries that help in the detection of rare variations over time intervals that occur in time series. Our research is strongly motivated by applications in financial domain.

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Cite

Text

Valentim et al. "Data Structures for Detecting Rare Variations in Time Series." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_45

Markdown

[Valentim et al. "Data Structures for Detecting Rare Variations in Time Series." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/valentim2012ecmlpkdd-data/) doi:10.1007/978-3-642-33486-3_45

BibTeX

@inproceedings{valentim2012ecmlpkdd-data,
  title     = {{Data Structures for Detecting Rare Variations in Time Series}},
  author    = {Valentim, Caio Dias and Laber, Eduardo Sany and Sotelo, David},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2012},
  pages     = {709-724},
  doi       = {10.1007/978-3-642-33486-3_45},
  url       = {https://mlanthology.org/ecmlpkdd/2012/valentim2012ecmlpkdd-data/}
}