Results 1 - 10 of 58

FTW: Fast Similarity Search under the Time Warping

by Yasushi Sakurai, Masatoshi Yoshikawa, Christos Faloutsos - Distance, Proceedings of PODS , 2005
"... Time-series data naturally arise in countless domains, such as meteorology, astrophysics, geology, multimedia, and eco-nomics. Similarity search is very popular, and DTW (Dy-namic Time Warping) is one of the two prevailing distance measures. Although DTW incurs a heavy computation cost, it provides ..."
Abstract - Cited by 29 (2 self) - Add to MetaCart
Time-series data naturally arise in countless domains, such as meteorology, astrophysics, geology, multimedia, and eco-nomics. Similarity search is very popular, and DTW (Dy-namic Time Warping) is one of the two prevailing distance measures. Although DTW incurs a heavy computation cost, it provides

Fast Time Series Classification Using Numerosity Reduction

by Xiaopeng Xi, Eamonn Keogh, Christian Shelton, Li Wei, Chotirat Ann Ratanamahatana - In ICML’06 , 2006
"... Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many realtime ..."
Abstract - Cited by 69 (12 self) - Add to MetaCart
Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many

Fast and exact warping of time series using adaptive segmental approximations

by Nikos Mamoulis, David W. Cheung, Eamonn Keogh - Machine Learning, Vol , 2005
"... Abstract. Similarity search is a core module of many data analysis tasks, including search by example, classification, and clustering. For time series data, Dynamic Time Warping (DTW) has been proven a very effective similarity measure, since it minimizes the effects of shifting and distortion in ti ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
Abstract. Similarity search is a core module of many data analysis tasks, including search by example, classification, and clustering. For time series data, Dynamic Time Warping (DTW) has been proven a very effective similarity measure, since it minimizes the effects of shifting and distortion

Fast approximate dynamic warping kernels

by G Nagendar, C. V. Jawahar - In ACM IKDD , 2015
"... The dynamic time warping (DTW) distance is a popular similarity measure for comparing time series data. It has been successfully applied in many fields like speech recognition, data mining and information retrieval to automatically cope with time deformations and variations in the length of the time ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
The dynamic time warping (DTW) distance is a popular similarity measure for comparing time series data. It has been successfully applied in many fields like speech recognition, data mining and information retrieval to automatically cope with time deformations and variations in the length

SparseDTW: A Novel Approach to Speed up Dynamic Time Warping

by Ghazi Al-naymat, Sanjay Chawla Javid Taheri , 2012
"... We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance be-tween two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea behi ..."
Abstract - Cited by 7 (1 self) - Add to MetaCart
We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance be-tween two time series that always yields the optimal result. This is in contrast to other known approaches which typically sacrifice optimality to attain space efficiency. The main idea

Efficient Processing of Warping Time Series Join of Motion Capture Data

by Yueguo Chen, Beng Chin Ooi
"... Abstract — Discovering non-trivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
sequence, the l-ε-join can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient l-ε-join of time series, we propose a two-step filter-and-refine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those

A Suffix Tree for Fast Similarity Searches of Time-Warped Sub-Sequences in Sequence Databases

by Sanghyun Park, Wesley W. Chu, Jeehee Yoon, Chihcheng Hsu , 1999
"... Several indexing techniques have been proposed to process similarity queries in sequence databases. Most of them focus on finding similar sequences of the same length using the Euclidean distance metric. However, in some applications where the elements of sequences may be sampled at different rates, ..."
Abstract - Cited by 3 (3 self) - Add to MetaCart
of categorization is applied to reduce index size and to accelerate query processing. A greater reduction of index size is achieved using a sparse suffix tree and more speed-up is attained by the fast estimation of the time warping distances between non-stored suffixes and a query sequence. Our method guarantees

Fast global alignment kernels

by Marco Cuturi , 2011
"... We propose novel approaches to cast the widely-used family of Dynamic Time Warping (DTW) distances and similarities as positive definite kernels for time series. To this effect, we provide new theoretical insights on the family of Global Alignment kernels introduced by Cuturi et al. (2007) and propo ..."
Abstract - Cited by 28 (0 self) - Add to MetaCart
We propose novel approaches to cast the widely-used family of Dynamic Time Warping (DTW) distances and similarities as positive definite kernels for time series. To this effect, we provide new theoretical insights on the family of Global Alignment kernels introduced by Cuturi et al. (2007

Efficient Classification of Long Time-Series

by Josif Grabocka, Erind Bedalli, Lars Schmidt-thieme
"... Abstract. Time-series classification has gained wide attention within the Machine Learning community, due to its large range of applicability varying from medical diagnosis, financial markets, up to shape and trajectory classification. The current state-of-art methods applied in timeseries classific ..."
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classification rely on detecting similar instances through neighboring algorithms. Dynamic Time Warping (DTW) is a similarity measure that can identify the similarity of two time-series, through the computation of the optimal warping alignment of time point pairs, therefore DTW is immune towards patterns shifted

Functional Subspace Clustering with Application to Time Series

by Mohammad Taha Bahadori, David Kale, Yingying Fan, Yan Liu
"... Functional data, where samples are random func-tions, are increasingly common and important in a variety of applications, such as health care and traffic analysis. They are naturally high dimen-sional and lie along complex manifolds. These properties warrant use of the subspace assump-tion, but most ..."
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learning problem as a sparse regression over operators. The result-ing problem can be efficiently solved via greedy variable selection, given access to a fast defor-mation oracle. We provide theoretical guaran-tees for FSC and show how it can be applied to time series with warped alignments. Experimen
Results 1 - 10 of 58