The UCR time series archive
By Hoang Anh Dau, Anthony J. Bagnall, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh. IEEE/CAA Journal of Automatica Sinica, 2018
1,107 citations recorded by the source corpus.
The UCR time series archive–introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline ( 1-nearest neighbor classification ), a fraction might be mis-attributing the reasons for their improvement. Moreover, the improvements claimed by these papers might have been achievable with a much simpler modification, requiring just a few lines of code.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/4543360d6b6133d143121eda079cd4b7667d2277
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Geoffrey I. Webb
- Angus Dempster
- Daniel F. Schmidt
- Franccois Petitjean
- Anthony J. Bagnall
- Jason Lines
- Aaron Bostrom
- James Large
In Computer Vision and Pattern Recognition
- Bixiong Xu
- Angus Dempster
- Pierre-Alain Muller
- Jonathan Weber
- Geoffrey I. Webb
- Hassan Ismail Fawaz
- Michael D. Flynn
- Daniel F. Schmidt
In Signal Processing
- Geoffrey I. Webb
- Angus Dempster
- Anthony J. Bagnall
- Jason Lines
- Franccois Petitjean
- Daniel F. Schmidt
- Aaron Bostrom
- Charlotte Pelletier
Citations in the corpus
- InceptionTime: Finding AlexNet for time series classification (2019, positive): “When evaluating global accuracy and computational complexity, we have used the UCR archive (Dau et al., 2018), which is the largest publicly available archive for TSC.”
- TS2Vec: Towards Universal Representation of Time Series (2021, methodology): “The UCR archive (Dau et al. 2019) and UEA archive (Bagnall et al.”
- MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification (2020, methodology): “Figure 1 shows the mean rank of MiniRocket in terms of accuracy versus other state-of-the-art methods over 30 resamples of 109 datasets from the UCR archive of benchmark time series [7].”
- Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, methodology): “They have been mainly developed by the machine learning community for various time series classification tasks present in the UCR archive, including image classification, speech recognition or electro-cardiogram (ECG) analysis [72].”
- DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, methodology): “(8) UCR is provided by the Multi-dataset Time Series Anomaly Detection Competition of KDD2021, and contains 250 sub-datasets from various natural sources [21, 36].”
- The UEA multivariate time series classification archive, 2018 (2018, background): “In October 2018 more datasets were added, bringing the total to 128 [1].”