k-Shape: Efficient and Accurate Clustering of Time Series

By John Paparrizos, Luis Gravano. SGMD, 2016

746 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/229f87832bc93cc4a4ba9a228e33135799dcf4bf

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. Anthony J. Bagnall
2. Zhicheng Cui
3. Yixin Chen
4. Eamonn J. Keogh
5. Chotirat (Ann) Ratanamahatana
6. Hoang Anh Dau
7. Chin-Chia Michael Yeh
8. Yan Zhu

In Signal Processing

1. Anthony J. Bagnall
2. Eamonn J. Keogh
3. Chotirat (Ann) Ratanamahatana
4. Yixin Chen
5. Zhicheng Cui
6. Hoang Anh Dau
7. Chin-Chia Michael Yeh
8. Yan Zhu

Citations in the corpus

• PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction (2023, methodology): “Then, we perform k-Shape clustering algorithm (Paparrizos and Gravano 2016) on these traffic flow series.”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “[72] proposed a novel method for clustering the time series”
• The UCR time series archive (2018, background): “We have little doubt that we could “dress up” our proposed FQT algorithm with spurious notation (Lipton and Steinhardt [17] call it “fancy mathematics”) to make it sound complex.”
• Multi-Scale Convolutional Neural Networks for Time Series Classification (2016, methodology): “Following the suggestions in [22], we z-normalize the following datasets during preprocessing: Beef, Coffee, Fish, OSULeaf and OliveOil.”