Time series classification with ensembles of elastic distance measures

By Jason Lines, Anthony J. Bagnall. Data mining and knowledge discovery, 2015

551 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0b72c321cd990df3b8960fc68e482d2f9e1834ab

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Citations in the corpus

AdaRNN: Adaptive Learning and Forecasting of Time Series (2021, methodology): “1 Time series analysis Traditional methods for time series classification or regression include distance-based [13, 32], feature-based [38], and ensemble methods [27].”
Unsupervised Scalable Representation Learning for Multivariate Time Series (2019, result): “We compare our scores to the ones of the four best classifiers of the state-of-the-art presented in Bagnall et al. (2017)4: COTE (Bagnall et al., 2015), ST (Bostrom & Bagnall, 2015), BOSS (Schäfer, 2015) and EE (Lines & Bagnall, 2015), on the first 85 datasets of the archive5.”
Using dynamic time warping distances as features for improved time series classification (2016, methodology): “…that have been proposed for time series classification, the decades old dynamic time warping (DTW) in combination with one-nearest neighbor has not been easy to beat convincingly (Xi et al. 2006; Ding et al. 2008; Xing et al. 2010; Chen et al. 2013; Wang et al. 2013; Lines and Bagnall 2014).”
Time series classification from scratch with deep neural networks: A strong baseline (2016, background): “The Elastic Ensemble (PROP) [6] combines 11 classifiers based on elastic distance measures with a weighted ensemble scheme.”
Multi-Scale Convolutional Neural Networks for Time Series Classification (2016, methodology): “We also select 11 existing methods with state-of-the-art results published within the recent three years, including: DTW with a warping window constraint set through cross validation (DTW CV) [23], Fast Shapelet (FS) [24] , SAX with vector space model (SV) [29], Bag-of-SFA-Symbols (BOSS) [26], Shotg”
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018, methodology): “Elastic Ensemble: The Elastic Ensemble (EE) algorithm is an ensemble approach composed of eleven 1-NN algorithms associated with eleven distance metrics, presenting different results on UCR datasets [74].”
Data Augmentation for Time Series Classification using Convolutional Neural Networks (2016, methodology): “Second, our methods reach competitive performance when compared to PROP [12] that is an ensemble classifier considered as one of the most efficient baselines for time series classification these days, and MCNN [6], a CNN for time series.”
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, methodology): “TSF and TSBF were evaluated on the original 46 UCR problems, LPS on an extended set of 75 data sets first used in Lines and Bagnall (2015) (with the standard single train/test splits).”
Deep learning for time series classification: a review (2018, methodology): “Lines and Bagnall (2015) compared several distance measures and showed that there is no single distance measure that significantly outperforms DTW.”
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles (2015, methodology): “For the time domain, we use Elastic Ensemble classiﬁer [12].”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “As outlined in a recent survey [154], early literature primarily focused on distance-based approaches [155], [156] and ensembling methods [157], [158].”