An online algorithm for segmenting time series

By Eamonn J. Keogh, Selina Chu, D. M. Hart, Michael J. Pazzani. Proceedings 2001 IEEE International Conference on Data Mining, 2001

1,260 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9849ff80a6e3d0ede2f726940901c70a88cb7116

How researchers see this paper

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

In Computer Networks and Communications

1. Yu Zheng
2. Nikos Mamoulis
3. Guoliang Li
4. Weidong (Tom) Cai
5. Fernando De la Torre
6. Philip Ka-Fai Chan
7. Nitesh V. Chawla
8. Diane Joyce Cook

In Signal Processing

1. Yu Zheng
2. Licia Capra
3. Guoliang Li
4. Ouri Wolfson
5. Tak-Chung Fu
6. Hai Yang
7. Haitao Yuan
8. Nikos Mamoulis

Citations in the corpus

• A Survey of Methods for Time Series Change Point Detection (2016, positive): “One clustering approach used for change point detection combines sliding window and bottom-up methods into an algorithm called sliding window and bottom-up (SWAB) [36].”
• Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (2017, background): “There is a large number of clustering algorithms in literature, which can be grouped into different perspectives, such as hierarchical [9, 51], centroid-based [20, 3, 27], graphbased [38, 28, 48, 25], sequential (temporal) [11, 37, 66], regression model based [7, 47], and subspace clustering models ”
• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (2018, background): “This paper is also related to other time series applications such as clustering/classification (Li and Prakash 2011; Hallac et al. 2017; Karim et al. 2018), segmentation (Keogh et al. 2001; Lemire 2007), and so on.”
• A review of change point detection methods (2018, methodology): “Besides, the authors of [77] have found that BotUp outperforms BinSeg on ten different data sets such as physiological signals (ECG), financial time-series (exchange rate), industrial monitoring (water levels), etc.”
• Heteroscedasticity and Autocorrelation Robust Structural Change Detection (2013, neutral): “See, for instance, Keogh et al. (2001) and Davis, Lee, and Rodriguez-Yam (2006).”
• Plato (2020, positive): “Among them, the Sliding-window ( SW ) has been proven to be more efficient than the Top-down and the Bottom-up methods [28, 29].”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “In particular, window-based algorithms [95, 96] maintain a growing sliding window for fitting spatial points with a line segment and continue to grow the sliding window until the approximation error exceeds some error bound.”
• Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion (2013, background): “In particular, there is a substantial amount of work in the field of data mining [27], [28], speech processing [29], [30], animal behavior analysis [12], [31], and signal processing [32], [33].”
• Trajectory Data Mining (2015, methodology): “One is the window-based algorithms, such as the Sliding Window algorithm [46] and Open Window algorithm [67].”
• A review on time series data mining (2011, methodology): “An ensemble-index, is proposed by Keogh et al. (2001b) and Vlachos et al. (2006), which ensembles two or more representations for indexing.”
• The effect of news and public mood on stock movements (2014, background): “The basic idea to address this problem is to plot (or approximate) the discrete data into a curve and then segment it into a series of straight lines [16,20,26,32].”
• Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news (2014, neutral): “5 [31] develops an improved online algorithm.”
• Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms (2004, methodology): “Partitioning algorithms, such as K-means and PAM [14], iteratively refine a set of k Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004) 1082-3409/04 $20.00 © 2004 IEEE clusters and do not scale well for larger data sets.”
• Selective review of offline change point detection methods (2020, methodology): “BotUp is often dubbed a “generous” method, by opposition to BinSeg, which is “greedy” [133].”
• Gesture spotting with body-worn inertial sensors to detect user activities (2008, background): “Visualisation of the relevant gestures (acted) as performed in case study 2: (a) cutlery, (b) drink, (c) spoon and (d) handheld.”
• Mining frequent spatio-temporal sequential patterns (2005, methodology): “[6] provides an online algorithm in splitting a sequence to segments with quite good quality.”