k-Shape: Efficient and Accurate Clustering of Time Series

By John Paparrizos, Luis Gravano. SIGMOD Conference, 2015

516 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8278ca04c4ffafef80abfbe0ce3c6cfc07b2792d

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

1. Yixin Chen
2. Zhicheng Cui
3. Wayne Xin Zhao
4. Jiawei Jiang
5. Jingyuan Wang
6. Chengkai Han
7. Luis Gravano
8. John Paparrizos

In Economics and Econometrics

1. Zhicheng Cui
2. Yixin Chen
3. Jiawei Jiang
4. Jingyuan Wang
5. Chengkai Han
6. Wayne Xin Zhao
7. Luis Gravano
8. John Paparrizos

In Signal Processing

1. Yixin Chen
2. Zhicheng Cui
3. Wayne Xin Zhao
4. Jiawei Jiang
5. Jingyuan Wang
6. Chengkai Han
7. John Paparrizos
8. Luis Gravano

Citations in the corpus

• k-Shape: Efficient and Accurate Clustering of Time Series (2016, methodology): “(Please refer to [53] for the full algorithm.”
• 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.”
• 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.”