FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

Wu, Renjie; Keogh, Eamonn

doi:10.1109/tkde.2020.3033752

Cited by 29 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modern speech recognition makes prevalent use of deep neural network models. Yet dynamic time warping has persisted as a useful method in data science [9,10] and cognitive science [11,12].…”

Section: Dynamic Time Warping As Absementmentioning

confidence: 99%

Using acoustic distance and acoustic absement to quantify lexical competition

Kelley

Tucker

2022

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

Using phonological neighborhood density has been a common method to quantify lexical competition. It is useful and convenient but has shortcomings that are worth reconsidering. The present study quantifies the effects of lexical competition during spoken word recognition using acoustic distance and acoustic absement rather than phonological neighborhood density. The indication of a word's lexical competition is given by what is termed to be its acoustic distinctiveness, which is taken as its average acoustic absement to all words in the lexicon. A variety of acoustic representations for items in the lexicon are analyzed. Statistical modeling shows that acoustic distinctiveness has a similar effect trend as that of phonological neighborhood density. Additionally, acoustic distinctiveness consistently increases model fitness more than phonological neighborhood density regardless of which kind of acoustic representation is used. However, acoustic distinctiveness does not seem to explain all of the same things as phonological neighborhood density. The different areas that these two predictors explain are discussed in addition to the potential theoretical implications of the usefulness of acoustic distinctiveness in the models. The present paper concludes with some reasons why a researcher may want to use acoustic distinctiveness over phonological neighborhood density in future experiments.

show abstract

Section: Dynamic Time Warping As Absementmentioning

confidence: 99%

Using acoustic distance and acoustic absement to quantify lexical competition

Kelley

Tucker

2022

The Journal of the Acoustical Society of America

View full text Add to dashboard Cite

show abstract

“…[28,41]) such as the Sakoe-Chiba Band and the Itakura parallelogram as these simplify the identification of the optimal warping path. While these are appealing concepts the authors in [42] observe that the wellknown FastDTW algorithm [41] is in fact slower than DTW.…”

Section: Dynamic Time Warpingmentioning

confidence: 99%

An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification

Alfke¹,

Gondos²,

Peroche³

et al. 2022

Front. Appl. Math. Stat.

View full text Add to dashboard Cite

Time series data play an important role in many applications and their analysis reveals crucial information for understanding the underlying processes. Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data. Two main aspects are studied in this paper. Namely, suitable distance measures to evaluate the similarities between different time series, and the choice of learning method to make predictions based on a given number of pre-labeled data points. However, the relationship between the two aspects has never been studied systematically in the context of graph-based learning. We describe four different distance measures, including (Soft) DTW and MPDist, a distance measure based on the Matrix Profile, as well as four successful semi-supervised learning methods, including the recently introduced graph Allen–Cahn method and Graph Convolutional Neural Network method. We provide results for the novel combination of these distance measures with both the Allen-Cahn method and the GCN algorithm for binary semi-supervised learning tasks for various time-series data sets. In our findings we compare the chosen graph-based methods using all distance measures and observe that the results vary strongly with respect to the accuracy. We then observe that no clear best combination to employ in all cases is found. Our study provides a reproducible framework for future work in the direction of semi-supervised learning for time series with a focus on graph representations.

show abstract

“…Pruning warping alignments is a valuable addition but has limited impact when many suitable warping paths exist. Approximate methods introduce an additional complexity that requires more memory and loses the computational simplicity of the original DTW algorithm making it often slow in practice [27]. In contrast, PQDTW is fast in its own right while maintaining compatibility with the aforementioned techniques.…”

Section: Dynamic Time Warping Dynamic Timementioning

confidence: 99%

Elastic Product Quantization for Time Series

Robberechts¹,

Meert²,

Davis³

2022

Preprint

View full text Add to dashboard Cite

Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate compact similarity-preserving representations of time series, enabling real-time similarity search on large in-memory data collections. However, the existing techniques are not ideally suited for assessing similarity when sequences are locally out of phase. In this paper, we propose the use of product quantization for efficient similarity-based comparison of time series under time warping. The idea is to first compress the data by partitioning the time series into equal length sub-sequences which are represented by a short code. The distance between two time series can then be efficiently approximated by pre-computed elastic distances between their codes. The partitioning into sub-sequences forces unwanted alignments, which we address with a pre-alignment step using the maximal overlap discrete wavelet transform (MODWT). To demonstrate the efficiency and accuracy of our method, we perform an extensive experimental evaluation on benchmark datasets in nearest neighbors classification and clustering applications. Overall, the proposed solution emerges as a highly efficient (both in terms of memory usage and computation time) replacement for elastic measures in time series applications.

show abstract

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

Cited by 29 publications

References 20 publications

Using acoustic distance and acoustic absement to quantify lexical competition

Using acoustic distance and acoustic absement to quantify lexical competition

An Empirical Study of Graph-Based Approaches for Semi-supervised Time Series Classification

Elastic Product Quantization for Time Series

Contact Info

Product

Resources

About