Logical-shapelets: an expressive primitive for time series classification

By Abdullah Mueen, Eamonn J. Keogh, Neal E. Young. Knowledge Discovery and Data Mining, 2011

346 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5ed965000e6692f9c8a214c0d76e59e674e4e8ae

How researchers see this paper

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

In Economics and Econometrics

1. Anthony J. Bagnall
2. Jason Lines
3. Aaron Bostrom
4. Rohit J. Kate
5. Eamonn J. Keogh
6. Charu C. Aggarwal
7. Jon Hills
8. Jessica Lin

In Signal Processing

1. Anthony J. Bagnall
2. Jason Lines
3. Aaron Bostrom
4. Rohit J. Kate
5. Charu C. Aggarwal
6. Jon Hills
7. James Large
8. Jessica Lin

Citations in the corpus

• 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.”
• Classification of time series by shapelet transformation (2013, methodology): “A method involving trading memory for speed is proposed in Mueen et al. (2011).”
• Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, methodology): “Logical shapelets [37], fast shapelets [6] and learnt shapelets [35].”
• 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.”
• k-Shape: Efficient and Accurate Clustering of Time Series (2015, neutral): “est in querying [2, 19, 45, 46, 48, 62, 74, 79], indexing [9, 13, 41, 42, 44, 77], classification [37, 56, 68, 88], clustering [43, 54, 64, 87, 89], and modeling [4, 38, 86] of such data.”
• The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, background): “Rakthanmanon and Keogh (2013) propose an extension of the decision tree shapelet approach (Ye and Keogh 2011; Mueen et al. 2011) that speeds up shapelet discov- ery.”
• 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.”
• 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).”