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SparseDTW: A Novel Approach to Speed up Dynamic Time Warping [article]

Ghazi Al-Naymat, Sanjay Chawla, Javid Taheri
2012 arXiv   pre-print
We present a new space-efficient approach, (SparseDTW), to compute the Dynamic Time Warping (DTW) distance between two time series that always yields the optimal result.  ...  The main idea behind our approach is to dynamically exploit the existence of similarity and/or correlation between the time series.  ...  Dynamic Time Warping (DTW) DTW is a dynamic programming technique used for measuring the similarity between any two time series with arbitrary lengths.  ... 
arXiv:1201.2969v1 fatcat:obt3bt2gkzhy7m5szxbqmbc7vm

Sparsification of the Alignment Path Search Space in Dynamic Time Warping [article]

Saeid Soheily-Khah , Pierre-François Marteau
2017 arXiv   pre-print
In this context, the Dynamic Time Warping (DTW) has enjoyed success in many domains, due to its 'temporal elasticity', a property particularly useful when matching temporal data.  ...  One major challenge that arises during temporal data analysis and mining is the comparison of time series or sequences, which requires to determine a proper distance or (dis)similarity measure.  ...  In fact, for Manhattan distance p = 1, for the Euclidean distance p = 2, while for the Maximum distance p = ∞. All the L p -norm distances do not consider the delay and time warp.  ... 
arXiv:1711.04453v1 fatcat:akgdhp3ovvcsjfu4u4krweiij4

Sparsification of the alignment path search space in dynamic time warping

Saeid Soheily-Khah, Pierre-François Marteau
2019 Applied Soft Computing  
In this context, the Dynamic Time Warping (DTW) has enjoyed success in many domains, due to its 'temporal elasticity', a property particularly useful when matching temporal data.  ...  One major challenge that arises during temporal data analysis and mining is the comparison of time series or sequences, which requires to determine a proper distance or (dis)similarity measure.  ...  In fact, for Manhattan distance p = 1, for the Euclidean distance p = 2, while for the Maximum distance p = ∞. All the L p -norm distances do not consider the delay and time warp.  ... 
doi:10.1016/j.asoc.2019.03.009 fatcat:vysfiquq6fe65any7qr53247ei

AWarp: Fast Warping Distance for Sparse Time Series

Abdullah Mueen, Nikan Chavoshi, Noor Abu-El-Rub, Hossein Hamooni, Amanda Minnich
2016 2016 IEEE 16th International Conference on Data Mining (ICDM)  
Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains.  ...  We derive a new time warping similarity measure (AWarp) for sparse time series that works on the run-length encoded representation of sparse time series.  ...  This dataset contains the review time series for 3,755 mobile apps. To form review time series, we collect the number of reviews an app receives in a day since the beginning of data availability.  ... 
doi:10.1109/icdm.2016.0046 dblp:conf/icdm/MueenCAHM16 fatcat:smpqztvq2ve5jh5u7g4z4uwtv4

Fast multisegment alignments for temporal expression profiles

Adam A Smith, Mark Craven
2008 Computational systems bioinformatics. Computational Systems Bioinformatics Conference  
We present two heuristics for speeding up a time series alignment algorithm that is related to dynamic time warping (DTW).  ...  In previous work, we developed our multisegment alignment algorithm to answer similarity queries for toxicogenomic time-series data.  ...  Dynamic Time Warping Dynamic time warping 4, 5 is often used for time series alignment problems. Briefly, this method computes an alignment matrix Γ from two series as shown in Figure 2 .  ... 
pmid:19642291 pmcid:PMC2766602 fatcat:v3272mgndzho5khvadcmnnieoq

FAST MULTISEGMENT ALIGNMENTS FOR TEMPORAL EXPRESSION PROFILES

Adam. A. Smith, Mark Craven
2008 Computational Systems Bioinformatics  
We present two heuristics for speeding up a time series alignment algorithm that is related to dynamic time warping (DTW).  ...  In previous work, we developed our multisegment alignment algorithm to answer similarity queries for toxicogenomic time-series data.  ...  Dynamic Time Warping Dynamic time warping 4, 5 is often used for time series alignment problems. Briefly, this method computes an alignment matrix Γ from two series as shown in Figure 2 .  ... 
doi:10.1142/9781848162648_0028 fatcat:c4wxohfw2bcwta5ox3chzkegtq

Dynamic Time Warping For Acoustic Response Interpolation: Possibilities And Limitations

Stephen Adams, F. Boland, Gavin Kearney, Claire Masterson
2009 Zenodo  
The 'distance' is the Euclidean distance between data point i in one time series and data point j in the other time series.  ...  A warp vector is created for each time series which describes how the signals are stretched.  ... 
doi:10.5281/zenodo.41816 fatcat:6cwkvov6h5hcjcchtnjzo7v3aa

Cartographing dynamic stall with machine learning

Matthew Lennie, Johannes Steenbuck, Bernd R. Noack, Christian Oliver Paschereit
2019 Wind Energy Science Discussions  
Modern data science/machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors.  ...  A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data.  ...  Dynamic time warping is a distance measurement that allows for squashing and stretching of the time series in order 185 to reach a best fit.  ... 
doi:10.5194/wes-2019-36 fatcat:a2h3nq2zrvdrfldcv3nbiliiyu

Comparing Cyclicity Analysis With Pre-established Functional Connectivity Methods to Identify Individuals and Subject Groups Using Resting State fMRI

Somayeh Shahsavarani, Ivan T. Abraham, Benjamin J. Zimmerman, Yuliy M. Baryshnikov, Fatima T. Husain
2020 Frontiers in Computational Neuroscience  
Such interactions are not easily captured by pre-established resting state functional connectivity methods including zero-lag correlation, lagged correlation, and dynamic time warping distance.  ...  For both patient and control groups, we found that the features generated by cyclicity and correlation (zero-lag and lagged) analyses were more reliable than the features generated by dynamic time warping  ...  ACKNOWLEDGMENTS The resting state fMRI data used in this work was collected at the Biomedical Imaging Center of the Beckman Institute for Advanced Science and Technology at the University of Illinois at  ... 
doi:10.3389/fncom.2019.00094 pmid:32038211 pmcid:PMC6984040 fatcat:diwhjsmqnjhhhiqd5ajebm5br4

Can Signature Biometrics Address Both Identification and Verification Problems?

Salman H. Khan, Zeashan Khan, Faisal Shafait
2013 2013 12th International Conference on Document Analysis and Recognition  
An elastic distance matching algorithm is then run over the presented data which declares the query signature as either genuine or forged based on the dissimilarity with the reference signature.  ...  Our results show that dynamic signatures can be accurately used for person identification along with the traditional verification methods.  ...  We have used Dynamic Time Warping (DTW) as a non-metric distance measure for verifying signatures. It dynamically warps the time axis to achieve a best alignment between two signature sequences.  ... 
doi:10.1109/icdar.2013.198 dblp:conf/icdar/KhanKS13 fatcat:xfgaldxfbjgupcnlw6wqcuv73a

Spatio-temporal Event Classification Using Time-Series Kernel Based Structured Sparsity [chapter]

László A. Jeni, András Lőrincz, Zoltán Szabó, Jeffrey F. Cohn, Takeo Kanade
2014 Lecture Notes in Computer Science  
We characterize spatio-temporal events as time-series of motion patterns and by utilizing time-series kernels we apply standard structured-sparse coding techniques to tackle this important problem.  ...  In many behavioral domains, such as facial expression and gesture, sparse structure is prevalent. This sparsity would be well suited for event detection but for one problem.  ...  Time-series Dictionary Building For the experiments on the gesture dataset, we used the time-series data provided within the dataset.  ... 
doi:10.1007/978-3-319-10593-2_10 pmid:27830214 pmcid:PMC5098425 fatcat:bpben4b2y5eybihq2sc4ekoyuq

Fast Exact Dynamic Time Warping on Run-Length Encoded Time Series [article]

Vincent Froese and Brijnesh Jain and Maciej Rymar and Mathias Weller
2020 arXiv   pre-print
Dynamic Time Warping (DTW) is a well-known similarity measure for time series.  ...  The standard dynamic programming approach to compute the DTW distance of two length-$n$ time series, however, requires~$O(n^2)$ time, which is often too slow for real-world applications.  ...  Conclusion We developed an asymptotically fast algorithm to compute exact DTW distances between runlength encoded time series. The running time is cubic in the maximum coding lengths of the inputs.  ... 
arXiv:1903.03003v5 fatcat:eqxf7nsewzf3rd4szooahmpzzu

Extracting Optimal Performance from Dynamic Time Warping

Abdullah Mueen, Eamonn Keogh
2016 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16  
Pazzani: Iterative Deepening Dynamic Time Warping for Time Series.  ...  The recursive version of cDTW 10 takes about 0.19 seconds for length 128 (see slide notes) A Mueen, N Chavoshi, N Abu-El-Rub, H Hamooni, A Minnich, Fast Warping Distance for Sparse Time Series, Technical  ...  • One-to-One comparison To solve the optimization problem for DTW distance, we need to perform simultaneous alignment of many time series. But, finding the optimal multiple alignment: 1.  ... 
doi:10.1145/2939672.2945383 dblp:conf/kdd/MueenK16 fatcat:o6ak5jxzdrcdvivvsyszr7etee

A Weighted Distance Measure for Calculating the Similarity of Sparsely Distributed Trajectories

Pekka Siirtola, Perttu Laurinen, Juha Röning
2008 2008 Seventh International Conference on Machine Learning and Applications  
The classifying accuracy of the proposed similarity measure was compared with three other methods, such as dynamic time warping, and it was noted that the new proposed method classifies instances mainly  ...  The method is especially designed for a situation where the points of the trajectories are distributed sparsely and at non-equidistant intervals.  ...  Acknowledgment The authors would like to thank the Finnish Funding Agency for Technology and Innovation and Infotech Oulu for funding this work. P.  ... 
doi:10.1109/icmla.2008.118 dblp:conf/icmla/SiirtolaLR08 fatcat:ymgrzrzervcxloackokqrkmf2a

Kernel sparse representation for time series classification

Zhihua Chen, Wangmeng Zuo, Qinghua Hu, Liang Lin
2015 Information Sciences  
Time warping is a general phenomenon in time series, which creates huge challenges for automatic time series classification.  ...  Dynamic time warping (DTW) was introduced to overcome the limitation of Euclidean distance [3, 48] . However, DTW is time consuming.  ...  To overcome the adverse influence of time shift, a number of effective elastic matching approaches such as dynamic time warp (DTW), edit distance with real penalty (ERP), and time warp edit distance (TWED  ... 
doi:10.1016/j.ins.2014.08.066 fatcat:y7dmuk37nzh25gnqqzn27i5lyy
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