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 feature-based 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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Esmael, B., Arnaout, A., Fruhwirth, R.K., Thonhauser, G. (2012). Multivariate Time Series Classification by Combining Trend-Based and Value-Based Approximations. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31128-4_29
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DOI: https://doi.org/10.1007/978-3-642-31128-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31127-7
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