Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these structured data. In this context, dynamic time warping (DTW) is probably the most common comparison measure. However, not much research effort has been put into improving it by learning. In this paper, we propose a nove...

Online signature verification is the process of using a dynamic system to confirm writer’s identity. It can be used as security entrance applications and password substitutes, well forensic tool assist an expert’s investigation. This study proposes novel online based on single-template strategy improve performance in real-world scenarios. employs discriminative mean template sets fusion strateg...

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...

Abstract Source datasets are selected with high similarity to target improve the effect of transfer learning. The low between source and may lead negative transfer. This paper proposes a similarity-based time series dataset selection method for First, reduce complexity operation, we use Dynamic time-warped barycentric averaging obtain prototype signals each convert calculation into signals. dyn...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...

Dear Editor, This letter proposes a new pattern matching method based on word embedding and dynamic time warping (DTW) to identify groups of similar alarm floods. First, messages are transformed into numeric values that represent alarms also reflect the relationships between occurrences. Then, similarities numerically encoded flood sequences calculated by DTW floods identified via clustering. T...

To overcome the computational complexity of the asynchronous Hidden Markov Model (AHMM), we present a novel multidimensional dynamic time warping (DTW) algorithm for hybrid fusion of asynchronous data. We show that our newly introduced multidimensional DTW concept requires significantly less decoding time while providing the same data fusion flexibility as the AHMM. Thus, it can be applied in a...

With the widespread use of time-lapse data to understand cellular function, there is a need for tools which facilitate high-throughput analysis of data. We present a system for automated segmentation and cell cycle phase labelling based on aligning temporal signals of simple features directly to a reference signal using Dynamic Time Warping (DTW). This is shown to result in a very accurate temp...

Multivariate time series (MTS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new dista...

In text-dependent speaker verification the speech signals have to be time-aligned. For that purpose dynamic time warping (DTW) can be used which performs the alignment by minimizing the Euclidean cepstral distance between the test and the reference utterance. While the cumulative Euclidean cepstral distance, which can be gathered from the DTW algorithm, could be used directly to discriminate be...