Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

Stopping Criterion Selection for Efficient Semi-supervised Time Series Classification

Abstract: High-quality classifiers generally require significant amount of labeled data. However, in many real-life applications and domains, labeled positive training data are difficult to obtain, while unlabeled data are largely available. To resolve the problem, many researchers have proposed semi-supervised learning methods that can build good classifiers by using only handful of labeled data. However, the main problem of the previous approaches for time series domains is the difficulty in selecting an optimal stopping criterion. This work therefore proposes a novel stopping criterion for semi-supervised time series classification, together with an integration of Dynamic Time Warping distance measure to improve the data selection during a self training. The experimental results show that this method can build a better classifier that achieves higher classification accuracy than the previous approach. In addition, the extended proposed work is shown to have satisfactory result for multi-cluster and multi-class semi-supervised time series classifier.

Handwritten character recognition using piecewise linear two-dimensional warping

Abstract: The effectiveness of piecewise linear 2D warping, a dynamic programming-based elastic image matching technique, in handwritten character recognition is investigated. The technique presented is capable of providing compensation for most variations in character patterns with tractable computation. The superiority of the present technique over several conventional 2D warping techniques in variation compensation is experimentally justified. Another comparison with monotonic and continuous 2D warping, a more flexible matching technique, reveals that the method presented takes far less computation than the latter, yet provides almost the same recognition accuracy for most categories.

Constrained distance based clustering for time-series: a comparative and experimental study

Abstract: Constrained clustering is becoming an increasingly popular approach in data mining. It offers a balance between the complexity of producing a formal definition of thematic classes-required by supervised methods-and unsupervised approaches, which ignore expert knowledge and intuition. Nevertheless, the application of constrained clustering to time-series analysis is relatively unknown. This is partly due to the unsuitability of the Euclidean distance metric, which is typically used in data mining, to time-series data. This article addresses this divide by presenting an exhaustive review of constrained clustering algorithms and by modifying publicly available implementations to use a more appropriate distance measure-dynamic time warping. It presents a comparative study, in which their performance is evaluated when applied to time-series. It is found that k-Means based algorithms become computationally expensive and unstable under these modifications. Spectral approaches are easily applied and offer state-of-the-art performance, whereas declarative approaches are also easily applied and guarantee constraint satisfaction. An analysis of the results raises several influencing factors to an algorithm's performance when constraints are introduced.

EventDTW: An Improved Dynamic Time Warping Algorithm for Aligning Biomedical Signals of Nonuniform Sampling Frequencies

Abstract: The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks.