Fast time series classification using numerosity reduction

Xi, Xiaopeng; Keogh, Eamonn; Shelton, Christian R.; Li, Wei; Ratanamahatana, Chotirat Ann

doi:10.1145/1143844.1143974

Cited by 436 publications

(297 citation statements)

References 18 publications

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“…This approach reduces the size of the training set by selecting the best representative instances and use only them during classification of new instances. Due to its advantages, instance selection has been explored for time-series classification [20].…”

Section: Introductionmentioning

confidence: 99%

“…For the above reasons, the proposed approach is denoted as Instance Selection based on Graph-coverage and Hubness for Time-series (INSIGHT). INSIGHT is evaluated experimentally with a collection of 37 publicly available time series classification data sets and is compared against FastAWARD [20], a state-of-the-art instance selection method for time series classification. We show that INSIGHT substantially outperforms FastAWARD both in terms of classification accuracy and execution time for performing the selection of instances.…”

Section: Introductionmentioning

confidence: 99%

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INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification

Búza

Νανόπουλος

Schmidt-Thieme

2011

Lecture Notes in Computer Science

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Abstract. Time-series classification is a widely examined data mining task with various scientific and industrial applications. Recent research in this domain has shown that the simple nearest-neighbor classifier using Dynamic Time Warping (DTW) as distance measure performs exceptionally well, in most cases outperforming more advanced classification algorithms. Instance selection is a commonly applied approach for improving efficiency of nearest-neighbor classifier with respect to classification time. This approach reduces the size of the training set by selecting the best representative instances and use only them during classification of new instances. In this paper, we introduce a novel instance selection method that exploits the hubness phenomenon in time-series data, which states that some few instances tend to be much more frequently nearest neighbors compared to the remaining instances. Based on hubness, we propose a framework for score-based instance selection, which is combined with a principled approach of selecting instances that optimize the coverage of training data. We discuss the theoretical considerations of casting the instance selection problem as a graph-coverage problem and analyze the resulting complexity. We experimentally compare the proposed method, denoted as INSIGHT, against FastAWARD, a state-of-the-art instance selection method for time series. Our results indicate substantial improvements in terms of classification accuracy and drastic reduction (orders of magnitude) in execution times.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification

Búza

Νανόπουλος

Schmidt-Thieme

2011

Lecture Notes in Computer Science

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show abstract

“…Dynamic Time Warping Despite the major effort spent in building accurate time series classifiers, still the nearest neighbor classifier combined with a similarity technique called Dynamic Time Warping (DTW) was reported to produce more accurate results [15]. DTW overcomes the drawback of other methods because it can detect pattern variations, such as translations/shifts, size and deformations.…”

Section: Time Series Classificationmentioning

confidence: 99%

“…end for 9: end for 10: svmM odel ← svm.train(Strain, θ) 11: return svmM odel -DTW-NN: Characterized as a hard-to-beat baseline in time series classification, which has been reported to achieve hard-to-beat classification accuracy [15]. The relative performance of our method compared to DTW-NN will give hints whether a refined maximum-margin is competitive, or not.…”

Section: Algorithm 4 Learnmodelmentioning

confidence: 99%

Invariant Time-Series Classification

Grabocka

Νανόπουλος

Schmidt-Thieme

2012

Machine Learning and Knowledge Discovery in Databases

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Abstract. Time-series classification is a field of machine learning that has attracted considerable focus during the recent decades. The large number of time-series application areas ranges from medical diagnosis up to financial econometrics. Support Vector Machines (SVMs) are reported to perform non-optimally in the domain of time series, because they suffer detecting similarities in the lack of abundant training instances. In this study we present a novel time-series transformation method which significantly improves the performance of SVMs. Our novel transformation method is used to enlarge the training set through creating new transformed instances from the support vector instances. The new transformed instances encapsulate the necessary intra-class variations required to redefine the maximum margin decision boundary. The proposed transformation method utilizes the variance distributions from the intra-class warping maps to build transformation fields, which are applied to series instances using the Moving Least Squares algorithm. Extensive experimentations on 35 time series datasets demonstrate the superiority of the proposed method compared to both the Dynamic Time Warping version of the Nearest Neighbor and the SVMs classifiers, outperforming them in the majority of the experiments.

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“…In particular, the problem of time-series classification and prediction is attracting a lot of attention among researchers. One of the most successful and popular methods for classification and prediction are kernelbased methods suchlike as support vector machines (SVM) [26,12,35,25]. Despite their popularity, there seem to be only a handful of kernels designed for time-series.…”

Section: Introductionmentioning

confidence: 99%

A multi-scale smoothing kernel for measuring time-series similarity

2015

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In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the kernel-derived distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world applications related to ozone concentration in atmosphere and electricity demand have been considered.

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Fast time series classification using numerosity reduction

Cited by 436 publications

References 18 publications

INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification

INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification

Invariant Time-Series Classification

A multi-scale smoothing kernel for measuring time-series similarity

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