Elastic Similarity and Distance Measures for Multivariate Time Series

2021-02-20 · Ahmed Shifaz, Charlotte Pelletier, Francois Petitjean, Geoffrey I. Webb

This paper contributes multivariate versions of seven commonly used elastic similarity and distance measures for time series data analytics. Elastic similarity and distance measures are a class of similarity measures that can compensate for misalignments in the time axis of time series data. We adapt two existing strategies used in a multivariate version of the well-known Dynamic Time Warping (DTW), namely, Independent and Dependent DTW, to these seven measures. While these measures can be applied to various time series analysis tasks, we demonstrate their utility on multivariate time series classification using the nearest neighbor classifier. On 23 well-known datasets, we demonstrate that each of the measures but one achieves the highest accuracy relative to others on at least one dataset, supporting the value of developing a suite of multivariate similarity and distance measures. We also demonstrate that there are datasets for which either the dependent versions of all measures are more accurate than their independent counterparts or vice versa. In addition, we also construct a nearest neighbor-based ensemble of the measures and show that it is competitive to other state-of-the-art single-strategy multivariate time series classifiers.

📄 PDF Abstract BibTeX arXiv:2102.10231

Code (1)

dotnet54/multivariate-measures 공식 구현

Tasks

ClassificationDynamic Time WarpingGeneral Classification Outlier DetectionTime SeriesTime Series Analysis Time Series Classification

Methods 이 논문이 사용한 방법론

DTW Dynamic Time Warping (DTW) [1] is one of well-known distance measures between a pairwise of time series. The main idea of DTW is to compute the distance from the matching of…

A Survey on Time-Series Distance Measures

2024-12-29 · John Paparrizos, Haojun Li, Fan Yang, Kaize Wu 외

Distance measures have been recognized as one of the fundamental building blocks in time-series analysis tasks, e.g., querying, indexing, classification, clustering, anomaly detection, and similarity search. The vast pro…

Anomaly Detection Survey Time Series Time Series Analysis

Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series

2025-10-21 · Elaina Rohlfing, Azim Ahmadzadeh, V Aparna arxiv

Solar-flare forecasting has been extensively researched yet remains an open problem. In this paper, we investigate the contributions of elastic distance measures for detecting patterns in the solar-flare dataset, SWAN-SF…

Elastic Product Quantization for Time Series

2022-01-04 · Pieter Robberechts, Wannes Meert, Jesse Davis

Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate compact similarity-preserving representat…

Quantization Time Series Time Series Analysis

Jiffy: A Convolutional Approach to Learning Time Series Similarity

2018-01-01 · ICLR 2018 1 · Divya Shanmugam, Davis Blalock, John Guttag

Computing distances between examples is at the core of many learning algorithms for time series. Consequently, a great deal of work has gone into designing effective time series distance measures. We present Jiffy, a sim…

Representation Learning Time Series Time Series Analysis

A Review and Evaluation of Elastic Distance Functions for Time Series Clustering

2022-05-30 · Chris Holder, Matthew Middlehurst, Anthony Bagnall

Time series clustering is the act of grouping time series data without recourse to a label. Algorithms that cluster time series can be classified into two groups: those that employ a time series specific distance measure…

Clustering Dynamic Time Warping Time Series Time Series Analysis+1