Segmenting Time Series: A Survey and Novel Approach

By Eamonn J. Keogh, Selina Chu, D. M. Hart, Michael J. Pazzani. 2002

708 citations recorded by the source corpus.

Subfields: Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/977f018e79163fc4fd63e2c12732ccbae0f1fdb8

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Citations in the corpus

A dynamic programming segmentation procedure for hydrological and environmental time series (2006, background): “Interest in time series segmentation problems has recently increased in the literature of data mining (see Himberg et al. 2001; Keogh 2001 and, especially, Keogh et al. 2003 which presents a good survey of segmentation methods appearing in the data mining literature).”
A Formal Study of Shot Boundary Detection (2007, background): “Similar problems exist in the related fields, such as temporal data segmentation [63], signal segmentation [64], and image segmentation [65].”
Selective review of offline change point detection methods (2020, neutral): “Win has been applied in numerous contexts: for instance, on biological signals [11, 114–117], on network data [28, 29], on speech time series [10, 11, 118] and on financial time series [3, 119, 120].”
Clustering of time series data - a survey (2005, methodology): “In this regard, most efforts have been devoted to data representation using so called segmentation algorithms[64].”
A Survey on Ambient-Assisted Living Tools for Older Adults (2013, methodology): “ple, extracting Fourier and wavelet features can be implemented on-chip using fast algorithms such as FFT [61].”
A review of change point detection methods (2018, neutral): “For instance, it has been applied on biological signals [28, 47, 63, 73, 131], on network data [94, 97], on speech time series [1, 42, 63] and on financial time series [22, 34, 78].”
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).”
1 Trend Filtering (2009, background): “Segmented regression arises in a variety of contexts, including abrupt change detection and time series segmentation (especially as a preprocessing step for mining time series databases); the reader is referred to a survey [57] and the references therein.”
Spatio-Temporal Data Mining (2017, background): “Change detection has been extensively studied in the context of time-series data, where the objective is to determine time intervals (segments) that exhibit homogeneous properties [Huang et al. 2013; Keogh et al. 2004; Liu et al. 2008; Mithal et al. 2012].”
Time-series clustering - A decade review (2015, background): “classification [6], visualization [7], segmentation [8], identifying patterns, trend analysis, summarization [9], and forecasting.”
Manifold Warp Segmentation of Human Action (2018, background): “According to [38], the time complexity of Bottom-Up algorithm is O(n log n/sn).”