A statistical technique for comparing the accuracies of several classifiers
Pattern Recognition Letters
Dynamic models for nonstationary signal segmentation
Computers and Biomedical Research
Unsupervised Learning Motion Models Using Dynamic Time Warping
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Proceedings of the VLDB Endowment
A time series representation model for accurate and fast similarity detection
Pattern Recognition
Assessing the uniqueness and permanence of facial actions for use in biometric applications
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on recent advances in biometrics
A symbolic representation method to preserve the characteristic slope of time series
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
The influence of global constraints on similarity measures for time-series databases
Knowledge-Based Systems
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Over recent years the popularity of time series has soared. Given the widespread use of modern information technology, a large number of time series may be collected during business, medical or biological operations, for example. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data, which in turn has resulted in a large number of works introducing new methodologies for indexing, classification, clustering and approximation of time series. In particular, many new distance measures between time series have been introduced. In this paper, we propose a new distance function based on a derivative. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than point-to-point function comparison. The new distance is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 20 time series datasets from a wide variety of application domains. Our experiments show that our method provides a higher quality of classification on most of the examined datasets.