Time series shapelets: a new primitive for data mining

By Lexiang Ye, Eamonn J. Keogh. Knowledge Discovery and Data Mining, 2009

1,175 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9c40543348fef37369c5b19ea26994ca1db8d9e8

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Citations in the corpus

Plant species identification using digital morphometrics: A review (2012, methodology): “Ye and Keogh (2009) used time-series shapelets.”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “The ubiquity of time series has generated a substantial interest in querying [2, 38, 39, 41, 52, 61, 65], indexing [8, 11, 34, 35, 37, 63], classification [30, 47, 58, 70], clustering [36, 45, 54, 69, 71], and modeling [3, 31, 68] of such data.”
Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models (2015, neutral): “, [15]).”
Outlier Analysis (2013, methodology): “second possibility proposed in [343, 490], is to mine shapelets from the data, which are highly discriminative features for classification purposes.”
TapNet: Multivariate Time Series Classification with Attentional Prototypical Network (2020, methodology): “For time series shapelet-based approaches (Rakthanmanon and Keogh 2013)(Wang et al. 2016)(Ye and Keogh 2009)(Fang, Wang, and Wang 2018), the models identify subsequences that are the most discriminating of the two classes.”
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification (2010, background): “the interpretability and efficiency of the classification, especially when the application contains high-dimensional, large-scale, or noisy datasets. The closest work is that of ( Geurts 2001 ).”
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).”
A Review on Outlier/Anomaly Detection in Time Series Data (2020, methodology): “Other dissimilarity-based techniques are based on shapelets, which are representative time series subsequences [Ye and Keogh 2009].”
ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis (2024, methodology): “Time series analysis is widely used in extensive applications, such as industrial forecasting (Zhou et al., 2021), missing value imputation (Friedman, 1962), action recognition (Ye & Keogh, 2009), and anomaly detection (Xu et al., 2021).”
A time series forest for classification and feature extraction (2013, background): “Ye and Keogh [23] brieﬂy discussed strategies of introducing additional measures to break ties, but it was in a diﬀerent context.”
A brief survey on sequence classification (2010, methodology): “Ye et al. [65] propose a feature selection method which can be applied directly on numeric time series.”
Exploiting multi-channels deep convolutional neural networks for multivariate time series classification (2015, methodology): “The most widely used algorithm is sliding window [17], by which a large number of redundant subsequences may be extracted and all kinds of potential subsequences in alignment-free pattern space are covered.”
Highly Comparative Feature-Based Time-Series Classification (2014, background): “Feature-based representations of time series are used across science, but are typically applied to longer time series corresponding to streams of data (such as extended medical or speech recordings) rather than the short pattern-like time series typically studied in temporal data mining.”
Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets (2016, background): “In order to mitigate this high time complexity, researchers have proposed various distance pruning techniques and candidate ranking approaches for both the admissible [27] and approximate [23] shapelet discovery.”
Spatio-Temporal Data Mining (2017, methodology): “This include methods that can identify temporally frequent sub-sequences that occur in a majority of time-series (e.g., temporal motifs [Mueen 2014]) or contain discriminatory information about a particular time-series class (e.g., shapelets [Hills et al. 2014; Ye and Keogh 2009]).”
A Bag-of-Features Framework to Classify Time Series (2013, background): “As an alternative, [10], [11], [12] proposed to identify subsequences (called shapelets) that are representative of a class using euclidean distance.”