Semi-supervised time series classification

Li, Wei; Keogh, Eamonn

doi:10.1145/1150402.1150498

Cited by 233 publications

(162 citation statements)

References 24 publications

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“…Moreover, there are strong evidences showing that the Euclidean distance is superior in accuracy comparing to other similarity measures [13,18,20]. In the rest of the paper, we assume the Euclidean distance, and, when the distance between two time series is concerned, the time series have the same length.…”

Section: Apcamentioning

confidence: 99%

A data-adaptive and dynamic segmentation index for whole matching on time series

Wang

Pei

et al. 2013

Proc. VLDB Endow.

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Similarity search on time series is an essential operation in many applications. In the state-of-the-art methods, such as the R-tree based methods, SAX and iSAX, time series are by default divided into equi-length segments globally, that is, all time series are segmented in the same way. Those methods then focus on how to approximate or symbolize the segments and construct indexes. In this paper, we make an important observation: global segmentation of all time series may incur unnecessary cost in space and time for indexing time series. We develop DSTree, a data adaptive and dynamic segmentation index on time series. In addition to savings in space and time, our new index can provide tight upper and lower bounds on distances between time series. An extensive empirical study shows that our new index DSTree supports time series similarity search effectively and efficiently.

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Section: Apcamentioning

confidence: 99%

A data-adaptive and dynamic segmentation index for whole matching on time series

Wang

Pei

et al. 2013

Proc. VLDB Endow.

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“…Vapnik referred to this as transduction [Vapn98], suggesting that it's better to focus on the simpler problem of classifying the current test examples rather than trying to construct a model to future test examples. For time series, Wei and Keogh [Wei06] proposed a semi-supervised classification technique based on 1-NN. The algorithm starts by training the classifier using all labeled data.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

Pattern Recognition in Time Series

Lin¹,

Williamson²,

Borne³

et al. 2012

Advances in Machine Learning and Data Mining for Astronomy

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Massive amount of time series data are generated daily, in areas as diverse as astronomy, industry, sciences, and aerospace, to name just a few. One obvious problem of handling time series databases concerns with its typically massive size-gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. Most classic data mining algorithms do not perform or scale well on time series data. The intrinsic structural characteristics of time series data such as the high dimensionality and feature correlation, combined with the measurement-induced noises that beset real-world time series data, pose challenges that render classic data mining algorithms ineffective and inefficient for time series. As a result, time series data mining has attracted enormous amount of attention in the past two decades.In this chapter, we discuss the state-of-the-art techniques for time series pattern recognition, the process of mapping an input representation for an entity or relationship to an output category. Approaches to pattern recognition tasks vary by representation for the input data, similarity/distance measurement function, and pattern recognition technique. We will discuss the various pattern recognition techniques, representations, and similarity measures commonly used for time series. While the majority of work concentrated on univariate time series, we will also discuss the applications of some of the techniques on multivariate time series.

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“…Li Wei and Eamonn Keogh [3], discussed about an approach for Time Series Classification by improving the Euclidean distance 1-Nearest Neighbor"s performance in case of binary classification where there are very few labeled data in the dataset. They did this by iteratively adding the instances that have already been classified with a high confidence value back to the training dataset.…”

Section: Introductionmentioning

confidence: 99%

Effect of Dynamic Time Warping using different Distance Measures on Time Series Classification

Kulkarni¹

2017

IJCA

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Time series classification involves classifying the time series according to the labels given in the training dataset. Time series data has features that are not completely independent of each other. Hence using algorithms such as Naïve Bayes or Support Vector Machines will not yield satisfying classification results due to the inherent assumption of feature independence of these algorithms. In such cases, similarity measures to find the similarity between the time series for classification can be opted. But there is an abundance of similarity measures available for finding the distance between two points. As the discussion is about time series data here, not all similarity measures can be applied to the data. One of the widely used distance measures, Euclidean distance, suffers when there are distortions in the time axis. Hence, this paper discusses about another widely used similarity measure called as Dynamic Time Warping for time series classification. Dynamic Time Warping itself uses a distance measure as one of the steps in the algorithm. This paper aims at comparing the various distance measures used for Dynamic Time Warping. The result obtained by the Dynamic Time Warping is provided to the K-Nearest Neighbor Classifier to achieve Time Series Classification.

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Semi-supervised time series classification

Cited by 233 publications

References 24 publications

A data-adaptive and dynamic segmentation index for whole matching on time series

A data-adaptive and dynamic segmentation index for whole matching on time series

Pattern Recognition in Time Series

Effect of Dynamic Time Warping using different Distance Measures on Time Series Classification

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