An accurate slicing method for dynamic time warping algorithm and the segment-level early abandoning optimization

Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible. In this paper, an alternating matrix with a concise structure is proposed to replace the complex three-dimensional matrix in LDTW and reduce the high complexity. Furthermore, an evolutionary chain tree is proposed to represent the warping paths and ensure an effective retrieval of the optimal one. Experiments using the benchmark platform offered by the University of California-Riverside show that our method uses 1.33% of the space, 82.7% of the time used by LDTW on average, which proves the efficiency of the proposed method.

/pdf/improved-ldtw-algorithm-based-on-the-alternating-matrix-and-3173pyk3.pdf

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree

FastOPM - A practical method for partial match of time series

This paper explores a way to verify that audio has not been maliciously tampered in a specific context: short viral videos taken from news recordings. Rather than trying to detect artifacts of tampering (internal inconsistency), we focus on positively verifying a query against a trusted source such as a news recording (external consistency). We propose a method for cross verifying a short audio query against a reference recording from which it was taken. Our approach is to define two hypotheses (non-tampered vs tampered), calculate the most likely alignment between query and reference for each hypothesis, and then perform a likelihood ratio test on the two alignments. We show that this method is fast to compute, much more robust than using MFCC features with Euclidean distance, and has the key benefit of explainability. Our cross verification approach provides an alternative perspective and complementary tool to existing tampering detection methods. 

Audio Cross Verification Using Dual Alignment Likelihood Ratio Test

Dynamic Time Warping (DTW) is an effective method that can be used to analyze community site visitors. DTW is a set of rules that measures the similarity between temporal sequences of various lengths. It maps these sequences onto one another and considers any nonlinear disparities in the time area to determine how intently they suit every other. In community visitor's evaluation, DTW can detect anomalies by detecting diffused adjustments in network site visitors, consisting of changes inside the behavior of an outsider or within the charge of malicious traffic go with the flow. Moreover, DTW may be used to analyze packet headers to stumble on malicious code or suspicious interests. By considering the detection of moderate deviations in the temporal pattern of packets, DTW may be used to come across subtle adjustments in the network site visitors. Ultimately, using DTW for analyzing network site visitors cannot most effectively improve the accuracy of detection but also offer clues as to the foundation of malicious pastime, assisting, in addition, telling the evaluation.

Exploring Dynamic Time Warping for Network Traffic Analysis

This paper investigates an ordered partial matching alignment problem, in which the goal is to align two sequences in the presence of potentially non-matching regions. We propose a novel parameter-free dynamic programming alignment method called hidden state time warping that allows an alignment path to switch between two different planes: a “visible” plane corresponding to matching sections and a “hidden” plane corresponding to non-matching sections. By defining two distinct planes, we can allow different types of time warping in each plane (e.g., imposing a maximum warping factor in matching regions while allowing completely unconstrained movements in non-matching regions). The resulting algorithm can determine the optimal continuous alignment path via dynamic programming, and the visible plane induces a (possibly) discontinuous alignment path containing matching regions. We show that this approach outperforms existing parameter-free methods on two different partial matching alignment problems involving speech and music.

/pdf/parameter-free-ordered-partial-match-alignment-with-hidden-imms1l8w.pdf

Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping

This article investigates several parallelizable alternatives to DTW for estimating the alignment between two long sequences. Whereas most previous work has focused on reducing the total computation and/or memory costs of DTW, our focus is instead on reducing wall clock time by utilizing common hardware like GPUs that are optimized for parallel processing. We propose and study four different parallelizable alignment algorithms: the first three algorithms compute approximations of DTW by breaking the pairwise cost matrix into rectangular regions and processing the regions in parallel, and the fourth algorithm computes an exact DTW alignment by processing the cost matrix along diagonals rather than rows or columns. We characterize the performance of our proposed alignment algorithms on an audio-audio alignment task, and we develop GPU-based implementations for the two best-performing algorithms, which we call weakly-ordered Segmental DTW (WSDTW) and Parallelized Diagonal DTW (ParDTW). Our experiments indicate that ParDTW is the most practical and useful of the four algorithms: it computes an exact DTW alignment and reduces runtime by 1.5 to 2 orders of magnitude on long sequences compared to current alternatives. We present a comprehensive evaluation and study of the alignment accuracy, runtime, and practical limitations of the proposed alignment algorithms.

A Study of Parallelizable Alternatives to Dynamic Time Warping for Aligning Long Sequences

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Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping

A Study of Parallelizable Alternatives to Dynamic Time Warping for Aligning Long Sequences