Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. Thismay lead tomisclassification especially in applica...

Averaging a set of time series is a major topic for many temporal data mining tasks as summarization, extracting prototype or clustering. Time series averaging should deal with the tricky multiple temporal alignment problem; a still challenging issue in various domains. This work compares the major progressive and iterative averaging time series methods under dynamic time warping (dtw).

SUMMARY The quality of seismic-to-well tie is commonly quantified using the classical Pearson's correlation coefficient. However the seismic wavelet is time-variant, well logging and upscaling is only approximate, and the correlation coefficient does not follow this nonlinear behavior. We introduce the Dynamic Time Warping (DTW) to automate the tying process, accounting for frequency and time v...

The quality of seismic-to-well tie is commonly quantified using the classical Pearson’s correlation coefficient. However the seismic wavelet is time-variant, well logging and upscaling is only approximate, and the correlation coefficient does not follow this nonlinear behavior. We introduce the Dynamic Time Warping (DTW) to automate the tying process, accounting for frequency and time variance....

This paper presents the use of Dynamic Time Warping (DTW) for measuring prosodic differences between variable-sized sentences. This methodological study may apply to various prosodic functions, accented or expressive speech. Both the structuring and attitudinal functions of prosody are investigated here. We evaluated the relevance of three prosodic (dis)similarity measures to account for percei...

In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW’s efficiency, together with multiple empirical studies showing that DTW-based cla...

The dynamic time warping (DTW) algorithm is able to find the optimal alignment between two time series. It is often used to determine time series similarity, classification, and to find corresponding regions between two time series. DTW has a quadratic time and space complexity that limits its use to only small time series data sets. In this paper we introduce FastDTW, an approximation of DTW t...

This paper presents a brief review on the lower bounding(LB) methods applied on Dynamic Time Warping(DTW) till now. Apart from providing a survey on the methods, an attempt has been made to compare these methods in terms of constraints involved with these methods. Some Lower Bounding (LB) methods have better pruning power than others, some are better in terms of running time and also there are ...

Dynamic Time Warping (DTW) is certainly the most relevant distance for time series analysis. However, its quadratic time complexity may hamper its use, mainly in the analysis of large time series data. All the recent advances in speeding up the exact DTW calculation are confined to similarity search. However, there is a significant number of important algorithms including clustering and classif...