In this paper, we propose a signature verification system based on Dynamic Time Warping (DTW). The method works by extracting the vertical projection feature from signature images and by comparing reference and probe feature templates using elastic matching. Modifications are made to the basic DTW algorithm to account for the stability of the various components of a signature. The basic DTW and...

Dynamic time warping (DTW) plays an important role in analytics on series. Despite the large body of research speeding up univariate DTW, method for multivariate DTW has not been improved much last two decades. The most popular algorithm used today is still one developed nineteen years ago. This paper presents a solution that, as far we know, first consistently outperforms classic across datase...

We present an algorithm for Dynamic Time Warping (DTW) on multi-dimensional time series (MDDTW). The algorithm utilises all dimensions to find the best synchronisation. It is compared to ordinary DTW, where a single dimension is used for aligning the series. Both one-dimensional and multidimensional DTW are also tested when derivatives instead of feature values are used for calculating the warp...

We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy. Unlike the Euclidean distance, DTW can compare time series of variable size and is robust to shifts or dilatations across the time dimension. To compute DTW, one typically solves a minimal-cost alignment problem between two time series using dynamic p...

Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) are two well-studied non-linear sequence alignment ( or, pattern matching) algorithm. The research trend transited from DTW to HMM in approximately 1988-1990, since DTW is deterministic and lack of the power to model stochastic signals. In this report, I make a comprehensive literature study into this transition, and show that DTW and sto...

Dynamic time warping, or DTW, is a powerful and domain-general sequence alignment method for computing a similarity measure. Such dynamic programming-based techniques like DTW are now the backbone and driver of most bioinformatics methods and discoveries. In neuroscience it has had far less use, though this has begun to change. We wanted to explore new ways of applying DTW, not simply as a meas...

Dynamic Time Warping (DTW) is used to find alignments between two related streams of information and can be used to link data, recognise patterns or find similarities. Typically, DTW requires the complete series of both input streams in advance and has quadratic time and space requirements. As such DTW is unsuitable for real-time applications and is inefficient for aligning long sequences. We p...

Considering personal privacy and difficulty of obtaining training material for many seldom used English words and (often non-English) names, language-independent (LI) with lightweight speaker-dependent (SD) automatic speech recognition (ASR) is a promising option to solve the problem. The dynamic time warping (DTW) algorithm is the state-of-the-art algorithm for small foot-print SD ASR applicat...

The Dynamic Time Warping (DTW) is a popular similarity measure between time series. The DTW fails to satisfy the triangle inequality and its computation requires quadratic time. Hence, to find closest neighbors quickly, we use bounding techniques. We can avoid most DTW computations with an inexpensive lower bound (LB Keogh). We compare LB Keogh with a tighter lower bound (LB Improved). We find ...

Given the ubiquity of time series data, the data mining community has spent significant time investigating the best time series similarity measure to use for various tasks and domains. After more than a decade of extensive efforts, there is increasing evidence that Dynamic Time Warping (DTW) is very difficult to beat. Given that, recent efforts have focused on making the intrinsically slow DTW ...