Time-series clustering - A decade review

By Saeed Reza Aghabozorgi, Ali Seyed Shirkhorshidi, Teh Ying Wah. 2015

1,740 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Signal Processing

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. Francisco Herrera
2. Salvador Garcia
3. Julián Luengo
4. José Manuel Benítez
5. Christoph Bergmeir
6. Sergio Ramírez-Gallego
7. Kasun Bandara
8. Yannis Papakonstantinou

In Economics and Econometrics

1. Norbert Marwan
2. Jürgen Kurths
3. Yong Zou
4. Reik V. Donner
5. Jonathan F. Donges
6. Yannis Papakonstantinou
7. Chunbin Lin
8. Etienne Boursier

In Signal Processing

1. Christoph Bergmeir
2. Yannis Papakonstantinou
3. Chunbin Lin
4. Etienne Boursier
5. Kasun Bandara
6. Norbert Marwan
7. Jürgen Kurths
8. Yong Zou

Citations in the corpus

• Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “, and many of these measures mostly concentrate on the shape of the respective time series (Aghabozorgi et al., 2015).”
• Big Data and cloud computing: innovation opportunities and challenges (2017, background): “Large, high-dimensional spatiotemporal data cannot be managed by existing data reduction algorithms within a tolerable time frame and acceptable quality (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015; García, Luengo, and Herrera 2015).”
• Tslearn, A Machine Learning Toolkit for Time Series Data (2020, positive): “It includes pre-processing routines, feature extractors, and machine learning models for classification (Bagnall et al., 2017; Fawaz et al., 2019), regression and clustering (Aghabozorgi et al., 2015).”
• A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data (2015, methodology): “Examples of distance-based clustering algorithms include partitioning clustering algorithms, such as k-means as well as k-medoids and hierarchical clustering [17].”
• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, background): “Also, many such measures mostly focus on the shape of the respective time series (Aghabozorgi et al., 2015), and they may yield unintuitive results and limited interpretability (Wang et al.”
• Complex network approaches to nonlinear time series analysis (2019, background): “From the perspective of time axis, time series clustering can be classified into three categories, whole time series clustering, subsequence clustering and time point clustering [11].”
• Big data preprocessing: methods and prospects (2016, background): “Developments on real-time applications can be also found in the literature [103].”
• Plato (2020, positive): “More precisely, the user provides (i) a time series T , (ii) a choice between fixed-length segmentation algorithm [14, 20] and variable-length/window-based segmentation algorithm [34, 28, 29, 5, 22, 39] , and (iii) the identifier of a compression function family (e.g., Polynomial function family).”
• The Zwicky Transient Facility: Science Objectives (2019, methodology): “Methods for time series clustering exist in other research areas such as statistics, finance, medicine, and economics, but not all of these new methods have made it to astronomy (for a review of time series clustering methods, see Aghabozorgi et al. 2015).”