iSAX: indexing and mining terabyte sized time series

By Jin Shieh, Eamonn J. Keogh. Knowledge Discovery and Data Mining, 2008

427 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b6f1b9de4df9f8bb52f056bbe0c2e536a843b1a0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Rohit J. Kate
2. Philippe Esling
3. Carlos Agón
4. Eamonn J. Keogh
5. Tak-Chung Fu
6. Charu C. Aggarwal
7. Tim Oates
8. Zhiguang Wang

In Signal Processing

1. Rohit J. Kate
2. Philippe Esling
3. Carlos Agón
4. Abdullah Mueen
5. Thanawin Rakthanmanon
6. Gustavo E. A. P. A. Batista
7. Eamonn J. Keogh
8. Bilson J. L. Campana

Citations in the corpus

• Using dynamic time warping distances as features for improved time series classification (2016, methodology): “The method has attracted much attention and has also been used and improved in multiple ways (Shieh and Keogh 2008; Ordónez et al. 2011; Senin and Malinchik 2013).”
• Time-series data mining (2012, methodology): “An extension of this approach, called indexable Symbolic Aggregate approXimation (iSAX) [Shieh and Keogh 2008], has been proposed to make fast index­ing possible by providing zero overlap at leaf nodes.”
• Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping (2012, methodology): “There exist data mining algorithms that can use a combination of (hopefully few) exact distance measures and (hopefully much faster) approximate searches to produce overall exact results [35].”