Rotation-invariant similarity in time series using bag-of-patterns representation

By Jessica Lin, Rohan Khade, Yuan Li. Journal of Intelligence and Information Systems, 2012

318 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6aad7940023151500673b45689d9e4a14349873c

How researchers see this paper

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

In Economics and Econometrics

1. Rohit J. Kate
2. Anthony J. Bagnall
3. Jason Lines
4. Aaron Bostrom
5. Jon Hills
6. James Large
7. Eamonn J. Keogh
8. Tim Oates

In Signal Processing

1. Rohit J. Kate
2. Anthony J. Bagnall
3. Jason Lines
4. Aaron Bostrom
5. James Large
6. Jon Hills
7. Tim Oates
8. Zhiguang Wang

Citations in the corpus

• Classification of time-series images using deep convolutional neural networks (2017, methodology): “Besides comparing with 1 − NNDTW , three state-of-the art TSC algorithms are also reported: Fast-Shapelets, 33 Bag-of-Patterns (BoP also known as BoF or BoW), 34,35 and SAX-VSM.”
• Deep Learning for Time-Series Analysis (2017, unknown): “1 with the state-of-theart SAX-BoP approach” [39], which are 50Words, Adiac, Beef, Coffee, ECG200, Face (all), Lightning-2, Lightning-7, OliveOil, OSU Leaf, Swedish Leaf and Yoga [11].”
• Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks (2014, methodology): “…1NN classifiers based on Euclidean distance and DTW, the recently proposed Fast-Shapelets based classifier (Rakthanmanon and Keogh 2013), the classifier based on Bag-of-Patterns (BoP) (Lin, Khade, and Li 2012; Oates et al. 2012) and the most recent SAX-VSM approach (Senin and Malinchik 2013).”
• Using dynamic time warping distances as features for improved time series classification (2016, methodology): “All comparisons were done using the 47 UCR time series classification datasets except the comparisons with (Lin et al. 2012) which had results for only 20 UCR datasets.”
• Time series classification with ensembles of elastic distance measures (2015, methodology): “Lin et al. (2012) propose a bag-of-patterns (BoP) approach that involves converting a time series into a discrete series using Symbolic Aggregate approximation (SAX) (Lin et al. 2007), creating a set of SAX words for each series through the application of a short sliding window, then using the…”
• The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, background): “OurBOP results are not significantly different to the publishedones.”
• Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, background): “The establishment of the UCR repository for TSC problems [1] has engendered growth in the number of algorithms proposed for TSC (for example, see [2], [3], [4], [5], [6], [7], [8], [9]).”