Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series

• URL: http://arxiv.org/abs/2212.06146v1
• Date: Fri, 9 Dec 2022 23:02:23 GMT
• Title: Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
• Authors: Audrey Der, Chin-Chia Michael Yeh, Renjie Wu, Junpeng Wang, Yan Zheng, Zhongfang Zhuang, Liang Wang, Wei Zhang, Eamonn Keogh
• Abstract summary: We introduce PRCIS, which stands for Pattern Representation Comparison in Series. PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries.
• Score: 18.205595410817327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentation, etc. For single-event time series Euclidean Distance and Dynamic Time Warping distance are known to be extremely effective. However, for time series containing cyclical behaviors, the semantic meaningfulness of such comparisons is less clear. For example, on two separate days the telemetry from an athlete workout routine might be very similar. The second day may change the order in of performing push-ups and squats, adding repetitions of pull-ups, or completely omitting dumbbell curls. Any of these minor changes would defeat existing time series distance measures. Some bag-of-features methods have been proposed to address this problem, but we argue that in many cases, similarity is intimately tied to the shapes of subsequences within these longer time series. In such cases, summative features will lack discrimination ability. In this work we introduce PRCIS, which stands for Pattern Representation Comparison in Series. PRCIS is a distance measure for long time series, which exploits recent progress in our ability to summarize time series with dictionaries. We will demonstrate the utility of our ideas on diverse tasks and datasets.

Related papers

• Inferring the Most Similar Variable-length Subsequences between Multidimensional Time Series [0.0]
  We propose an algorithm that provides the exact solution of finding the most similar multidimensional subsequences between time series.<n>The algorithm is built based on theoretical guarantee of correctness and efficiency.<n>In real-world datasets, it extracted the most similar subsequences even faster.
  arXiv  Detail & Related papers  (2025-05-16T10:39:46Z)
• Multiscale Dubuc: A New Similarity Measure for Time Series [1.024113475677323]
  We introduce the Multiscale Dubuc Distance measure and prove that it is a metric. We use 95 datasets from the UCR Time Series Classification Archive to compare MDD's performance with EuD, LCSS, and DTW. Our experiments show that MDD's overall success, without any case-specific customization, is comparable to DTW with optimized window sizes per dataset.
  arXiv  Detail & Related papers  (2024-11-15T18:38:18Z)
• Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making [66.27188304203217]
  Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning. Prior attempts to define such temporal distances in settings have been stymied by an important limitation. We show how successor features learned by contrastive learning form a temporal distance that does satisfy the triangle inequality.
  arXiv  Detail & Related papers  (2024-06-24T19:36:45Z)
• TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling [67.02157180089573]
  Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. This paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
  arXiv  Detail & Related papers  (2024-02-04T13:10:51Z)
• Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data [2.845817138242963]
  Time series clustering is an essential machine learning task with applications in many disciplines. A distance between circular series is introduced and used to construct a clustering procedure. A fuzzy approach is adopted, which enables the procedure to locate each series into several clusters with different membership degrees.
  arXiv  Detail & Related papers  (2024-01-26T12:21:57Z)
• A Review and Evaluation of Elastic Distance Functions for Time Series Clustering [0.0]
  We describe nine commonly used elastic distance measures and compare their performance with k-means and k-medoids clustering. The most popular technique, dynamic time warping (DTW), performs worse than Euclidean distance with k-means, and even when tuned, is no better. Our conclusion is to recommend MSM with k-medoids as the benchmark algorithm for clustering time series with elastic distance measures.
  arXiv  Detail & Related papers  (2022-05-30T15:32:55Z)
• Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting--Full Version [50.43914511877446]
  We propose a triangular, variable-specific attention to ensure high efficiency and accuracy. We show that Triformer outperforms state-of-the-art methods w.r.t. both accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-04-28T20:41:49Z)
• Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series [29.83535690719436]
  We show that it is possible to efficiently perform inter-time series similarity joins with error bounded guarantees by creating a compact "dictionary" representation of time series. We demonstrate the utility of our dictionary-based inter-time series similarity joins on domains as diverse as medicine and transportation.
  arXiv  Detail & Related papers  (2021-12-24T06:43:26Z)
• Kernel distance measures for time series, random fields and other structured data [71.61147615789537]
  kdiff is a novel kernel-based measure for estimating distances between instances of structured data. It accounts for both self and cross similarities across the instances and is defined using a lower quantile of the distance distribution. Some theoretical results are provided for separability conditions using kdiff as a distance measure for clustering and classification problems.
  arXiv  Detail & Related papers  (2021-09-29T22:54:17Z)
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting [69.38716332931986]
  We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
  arXiv  Detail & Related papers  (2021-09-14T07:38:35Z)
• Elastic Similarity Measures for Multivariate Time Series Classification [4.5669999076671655]
  Elastic similarity measures are a class of similarity measures specifically designed to work with time series data. Elastic similarity measures are widely used in machine learning tasks such as classification, clustering and outlier detection.
  arXiv  Detail & Related papers  (2021-02-20T02:24:33Z)
• Aligning Time Series on Incomparable Spaces [83.8261699057419]
  We propose Gromov dynamic time warping (GDTW), a distance between time series on potentially incomparable spaces. We demonstrate its effectiveness at aligning, combining and comparing time series living on incomparable spaces.
  arXiv  Detail & Related papers  (2020-06-22T22:19:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.