dtw   234

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Twitter
RT : The snow is causing delays and cancellations at . Remember to check with your airline before heading to the air…
DTW  from twitter_favs
2 days ago by vielmetti
Twitter
At where cancelled my to flight, conveniently just a few minutes after I checked my bag.

R…
EWR  DTW  from twitter
2 days ago by vielmetti
Twitter
Hey - what are the current plans for to ? You are showing it 10 minutes delayed, which se…
DTW  EWR  UA3530  from twitter
2 days ago by vielmetti
Twitter
Working to see what happened with flight from to . Passenger on-board said a tow truck som…
DTW  dl2421  RDU  from twitter_favs
4 days ago by vielmetti
Twitter
Oakland international airport waiting for flight to
dtw  from twitter
11 weeks ago by vielmetti
[1705.05681] Optimal Warping Paths are unique for almost every pair of Time Series
"An optimal warping path between two time series is generally not unique. The size and form of the set of pairs of time series with non-unique optimal warping path is unknown. This article shows that optimal warping paths are unique for almost every pair of time series in a measure-theoretic sense. All pairs of time series with non-unique optimal warping path form a negligible set and are geometrically the union of zero sets of quadratic forms. The result is useful for analyzing and understanding adaptive learning methods in dynamic time warping spaces."
papers  time-series  alignment  dtw 
may 2017 by arsyed
[1703.01141] Dynamic State Warping
"The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping and returns a distance value. DTW shows superior ability in many applications, e.g. video, image, etc. However, in DTW, two points are paired essentially based on point-to-point Euclidean distance (ED) without considering the autocorrelation of sequences. Thus, points with different semantic meanings, e.g. peaks and valleys, may be matched providing their coordinate values are similar. As a result, DTW is sensitive to noise and poorly interpretable. This paper proposes an efficient and flexible sequence alignment algorithm, dynamic state warping (DSW). DSW converts each time point into a latent state, which endows point-wise autocorrelation information. Alignment is performed by using the state sequences. Thus DSW is able to yield alignment that is semantically more interpretable than that of DTW. Using one nearest neighbor classifier, DSW shows significant improvement on classification accuracy in comparison to ED (70/85 wins) and DTW (74/85 wins). We also empirically demonstrate that DSW is more robust and scales better to long sequences than ED and DTW."
papers  time-series  sequence  alignment  dtw 
may 2017 by arsyed
Twitter
Dinner is a capricciosa pizza and a glass of Montepulciano at . Headed to Miami Beach w/…
DTW  from twitter
march 2017 by peterhoneyman
[1703.01541] Soft-DTW: a Differentiable Loss Function for Time-Series
"We propose in this paper a differentiable learning loss between time series. Our proposal builds upon the celebrated Dynamic Time Warping (DTW) discrepancy. Unlike the Euclidean distance, DTW is able to compare asynchronous time series of varying size and is robust to elastic transformations in time. To be robust to such invariances, DTW computes a minimal cost alignment between time series using dynamic programming. Our work takes advantage of a smoothed formulation of DTW, called soft-DTW, that computes the soft-minimum of all alignment costs. We show in this paper that soft-DTW is a differentiable loss function, and that both its value and its gradient can be computed with quadratic time/space complexity (DTW has quadratic time and linear space complexity). We show that our regularization is particularly well suited to average and cluster time series under the DTW geometry, a task for which our proposal significantly outperforms existing baselines (Petitjean et al., 2011). Next, we propose to tune the parameters of a machine that outputs time series by minimizing its fit with ground-truth labels in a soft-DTW sense."
papers  time-series  dtw 
march 2017 by arsyed

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