A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ISITC '07 Proceedings of the 2007 International Symposium on Information Technology Convergence
Escalation: complex event detection in wireless sensor networks
EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A Pattern Mining Approach for Classifying Multivariate Temporal Data
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Continuous trend-based classification of streaming time series
ADBIS'05 Proceedings of the 9th East European conference on Advances in Databases and Information Systems
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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.