Fast time series classification using numerosity reduction

By Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton, Li Wei, Chotirat (Ann) Ratanamahatana. International Conference on Machine Learning, 2006

658 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/415dae363449d399141d07941424b057a26716aa

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Citations in the corpus

TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering (2008, methodology): “The 1-nearest neighbor classifler using dynamic time warping(DTW) is known to be the best method[20, 21 ].”
A Bag-of-Features Framework to Classify Time Series (2013, methodology): “Despite the fact that finding DTW distance without any modification on the algorithm is known to be computationally demanding for many applications [54], fast lower bounding function is used by [23] to prune the time series that cannot be the best match.”
Using dynamic time warping distances as features for improved time series classification (2016, methodology): “Xi et al. (2006) give a brief review of various techniques that were proposed and how they do not satisfactorily beat 1NN-DTW-R.”
Weighted dynamic time warping for time series classification (2011, methodology): “In this paper, we use 1-nearest neighbor classifier because the 1-nearest neighbor classifier with DTW showed very competitive performance and has been widely used for time series classification [26].”
Highly Comparative Feature-Based Time-Series Classification (2014, neutral): “benchmark for classification performance [33].”
iSAX: indexing and mining terabyte sized time series (2008, methodology): “We can see this algorithm as an anytime algorithm [17].”
A brief survey on sequence classification (2010, methodology): “It is shown that comparing to a wide range of classiﬁers, such as neural networks, SVM and HMM, 1-nearest neighbor classiﬁer with dynamic time warping distance is usually superior in classiﬁcation accuracy [61].”
A review on time series data mining (2011, background): “Also, Xi et al. (2006) propose a semi-supervised time series classifiers when only a small set of labeled examples is available.”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “, the best published results (by a large margin) come from simple nearest neighbor methods [42].”
Exploiting multi-channels deep convolutional neural networks for multivariate time series classification (2015, methodology): “By limiting the warping path to a warping window, some previous work [10,19] showed that relatively tight warping windows actually improve the classification accuracy.”
Learning Discriminative Fisher Kernels (2011, background): “(3)We note here that for the special case of time series, edit distances and dynamic time warping actually perform very well (Xi et al., 2006; Ding, 2008).”
Time series shapelets: a novel technique that allows accurate, interpretable and fast classification (2010, methodology): “Among all the time series classification algorithms, the simple nearest neighbor algorithm has been shown to be the most robust and accurate method by experiments on a great variety of datasets (Xi et al. 2006).”
Time series shapelets: a new primitive for data mining (2009, positive): “Time Series Shapelets: A New Primitive for Data Mining Lexiang Ye Dept. of Computer Science & Engineering University of California, Riverside, CA 92521 lexiangy@cs.ucr.edu ABSTRACT Classification of time series has been attracting great interest over the past decade.”
Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks (2014, methodology): “While another part of researchers tried to reduce the computation of 1-NN by constructing data dictionary [12, 7, 14, 22].”