Continuous Trend-Based Classification of Streaming Time Series

Kontaki, Maria; Papadopoulos, Apostolos N.; Manolopoulos, Yannis

doi:10.1007/11547686_22

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…Kontaki et al [10] propose using PLA to transform the time series to a vector of symbols (U and D) denoting the trend of the series. Keogh and Pazzani [8] suggest a representation that consists of piecewise linear segments to represent a shape; and a weight vector that contains the relative importance of each individual linear segment.…”

Section: State Of the Artmentioning

confidence: 99%

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Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations

Esmael

Arnaout²,

Frühwirth³

et al. 2012

Computational Science and Its Applications – ICCSA 2012

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Abstract. Multivariate time series data often have a very high dimensionality. Classifying such high dimensional data poses a challenge because a vast number of features can be extracted. Furthermore, the meaning of the normally intuitive term "similar to" needs to be precisely defined. Representing the time series data effectively is an essential task for decision-making activities such as prediction, clustering and classification. In this paper we propose a featurebased classification approach to classify real-world multivariate time series generated by drilling rig sensors in the oil and gas industry. Our approach encompasses two main phases: representation and classification.For the representation phase, we propose a novel representation of time series which combines trend-based and value-based approximations (we abbreviate it as TVA). It produces a compact representation of the time series which consists of symbolic strings that represent the trends and the values of each variable in the series. The TVA representation improves both the accuracy and the running time of the classification process by extracting a set of informative features suitable for common classifiers.For the classification phase, we propose a memory-based classifier which takes into account the antecedent results of the classification process. The inputs of the proposed classifier are the TVA features computed from the current segment, as well as the predicted class of the previous segment.Our experimental results on real-world multivariate time series show that our approach enables highly accurate and fast classification of multivariate time series.

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Section: State Of the Artmentioning

confidence: 99%

“…In addition, trend-ba es is closer to human intuition [10]. ed approximation, the least squares method is used to f t of data points.…”

Section: Trend-based Apprmentioning

confidence: 99%

Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations

Esmael

Arnaout²,

Frühwirth³

et al. 2012

Computational Science and Its Applications – ICCSA 2012

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“…First, trend features are an important characteristic of a time series. In some applications, the way that time series values vary is considered to be very important, because it enables useful conclusions to be drawn (Kontaki et al, 2005). For example, in a satellite fault monitoring system it is important to know which telemetry parameters show an increasing trend and which show a decreasing trend to avoid serious failures.…”

Section: Problem Statementmentioning

confidence: 99%

“…Trendbased approximations have been studied extensively in the last decade. For example, Kontaki et al (2005) used piecewise linear approximation (PLA) to transform a time series into a vector of symbols (using U to indicate an upward trend and D for a downward trend). In this paper, three kinds of trend (Fig.…”

Section: Symbolic Representation Based On Trend Featuresmentioning

confidence: 99%

Symbolic representation based on trend features for knowledge discovery in long time series

Yin

Yang

Zhu

et al. 2015

Frontiers Inf Technol Electronic Eng

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The symbolic representation of time series has attracted much research interest recently. The high dimensionality typical of the data is challenging, especially as the time series becomes longer. The wide distribution of sensors collecting more and more data exacerbates the problem. Representing a time series effectively is an essential task for decision-making activities such as classification, prediction, and knowledge discovery. In this paper, we propose a new symbolic representation method for long time series based on trend features, called trend feature symbolic approximation (TFSA). The method uses a two-step mechanism to segment long time series rapidly. Unlike some previous symbolic methods, it focuses on retaining most of the trend features and patterns of the original series. A time series is represented by trend symbols, which are also suitable for use in knowledge discovery, such as association rules mining. TFSA provides the lower bounding guarantee. Experimental results show that, compared with some previous methods, it not only has better segmentation efficiency and classification accuracy, but also is applicable for use in knowledge discovery from time series.

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“…Trend analysis has been used in the past in static and streaming time series [9,12,8]. We use trends as a base to cluster streaming time series for two reasons.…”

Section: Introductionmentioning

confidence: 99%

Continuous Trend-Based Clustering in Data Streams

Kontaki

Papadopoulos

Manolopoulos

Lecture Notes in Computer Science

Self Cite

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Abstract. Trend analysis of time series is an important problem since trend identification enables the prediction of the near future. In streaming time series the problem is more challenging due to the dynamic nature of the data. In this paper, we propose a method to continuously clustering a number of streaming time series based on their trend characteristics. Each streaming time series is transformed to a vector by means of the Piecewise Linear Approximation (PLA) technique. The PLA vector comprises pairs of values (timestamp, trend) denoting the starting time of the trend and the type of the trend (either UP or DOWN) respectively. A distance metric for PLA vectors is introduced. We propose split and merge criteria to continuously update the clustering information. Moreover, the proposed method handles outliers. Performance evaluation results, based on real-life and synthetic data sets, show the efficiency and scalability of the proposed scheme.

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Continuous Trend-Based Classification of Streaming Time Series

Cited by 13 publications

References 15 publications

Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations

Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations

Symbolic representation based on trend features for knowledge discovery in long time series

Continuous Trend-Based Clustering in Data Streams

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