Pattern Extraction for Time Series Classification

By Pierre Geurts. European Conference on Principles of Data Mining and Knowledge Discovery, 2001

349 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5c8c63ee0da654f7ae3cc296cc5d8fc3200a433b

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Citations in the corpus

A Bag-of-Features Framework to Classify Time Series (2013, methodology): “In this direction, Geurts [15] used the discontinuity points from a piecewise-linear approximation of the time series to detect patterns and classify the series.”
Decision Trees for Functional Variables (2006, methodology): “Although our prediction accuracy isn't quite at par with the best methods for this problem (see Table 2) it is better than that of the other functional classifier that produces interpretable outputs, Geurts (2001).”
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration (2002, background): “SEGMENTATION • Cylinder-Bell-Funnel: This synthetic dataset has been in the literature for 8 years, and has been cited at least a dozen times [21].”
A time series forest for classification and feature extraction (2013, background): “Geurts [7] extracted local temporal properties after discretizing the time series.”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “eration algorithms proposed in the original papers [15, 16].”
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification (2010, methodology): “(A similar idea is illustrated in (Geurts 2001).)”
Artificial intelligence in medicine (1989, positive): “Geurts [57] proposed a feature-based approach that integrates extracted temporal patterns into decision trees.”
On The Marriage of Lp-norms and Edit Distance (2004, methodology): “In the table below, we show the average classification error rate using three benchmarks: the Cylinder-Bell-Funnel (CBFtr) data [12, 14], the ASL data [25] and the “cameramouse” (CM) data [25].”
Experimental comparison of representation methods and distance measures for time series data (2010, methodology): “Because these are synthetic data sets, one has the luxury of creating as many instances as needed, using the data generation algorithms proposed in the original papers [21, 22].”
Using dynamic time warping distances as features for improved time series classification (2016, methodology): “Under the feature-based methods, a statistical (Nanopoulos et al. 2001; Geurts 2001) or symbolic (Lin et al. 2007; Ye and Keogh 2009) feature representation is first defined for time series.”
Rotation-invariant similarity in time series using bag-of-patterns representation (2012, background): “Classification of time series has attracted much interest from the data mining community (Geurts 2001; Keogh and Kasetty 2002; Nanopoulos et al. 2001; Ratanamahatana and Keogh 2004; Radovanovic et al. 2010; Wei and Keogh 2006; Ye and Keogh 2009; Mueen et al. 2011; Xing et al. 2011).”
Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets (2013, background): “bioinformatics), including class prototypes [6], discriminative patterns [2][5][8], and predictive motifs [18], etc.”
2007 Special Issue: Optimization and applications of echo state networks with leaky- integrator neurons (2007, neutral): “Various techniques have been applied to this problem (Kudo, Toyama, & Shimbo, 1999) (Geurts, 2001) (Barber, 2003) (Strickert, 2004).”
Time series shapelets: a new primitive for data mining (2009, neutral): “While there is a vast amount of literature on time series classification and mining [4][7][14], we believe that the problem we intend to solve here is unique. The closest work is that of [ 5 ].”
A symbolic representation of time series, with implications for streaming algorithms (2003, background): “Many researchers have also considered transforming real valued time series into symbolic representations, noting that such representations would potentially allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities, in…”
Multivariate LSTM-FCNs for Time Series Classification (2018, background): “Multivariate time series classifications are applied in healthcare [8], phoneme classification [9], activity recognition, object recognition, and action recognition [10, 11, 12, 13].”
A review on time series data mining (2011, background): “Geurts (2001) proposes to classify time series data based on combining local properties or patterns in the time series.”
Time series feature extraction for data mining using DWT and DFT (2003, neutral): “Classification of time series is done in [26] by generating rules with inductive logic programming and in [11] by regression trees and decision trees.”