LoCoMotif: Discovering time-warped motifs in time series

2023-11-29 · Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel

Time Series Motif Discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time series. All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series. In this paper, we present a new method, LoCoMotif, that has none of these limitations. The method is motivated by a concrete use case from physiotherapy. We demonstrate the value of the proposed method on this use case. We also introduce a new quantitative evaluation metric for motif discovery, and benchmark data for comparing TSMD methods. LoCoMotif substantially outperforms the existing methods, on top of being more broadly applicable.

📄 PDF Abstract BibTeX arXiv:2311.17582

Code (1)

ml-kuleuven/locomotif 공식 구현

Tasks

Time Series

Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery

2025-02-17 · Aras Yurtman, Daan Van Wesenbeeck, Wannes Meert, Hendrik Blockeel

Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To address this, we propose a framework that …

Time Series

Efficiently Discovering Frequent Motifs in Large-scale Sensor Data

2015-01-02 · Puneet Agarwal, Gautam Shroff, Sarmimala Saikia, Zaigham Khan

While analyzing vehicular sensor data, we found that frequently occurring waveforms could serve as features for further analysis, such as rule mining, classification, and anomaly detection. The discovery of waveform patt…

Anomaly Detection Clustering Time Series Time Series Analysis

Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series

2019-11-20 · Yifeng Gao, Jessica Lin

Detecting repeating patterns of different lengths in time series, also called variable-length motifs, has received a great amount of attention by researchers and practitioners. Despite the significant progress that has b…

Time Series Time Series Analysis

MASA: Motif-Aware State Assignment in Noisy Time Series Data

2018-09-06 · Saachi Jain, David Hallac, Rok Sosic, Jure Leskovec

Complex systems, such as airplanes, cars, or financial markets, produce multivariate time series data consisting of a large number of system measurements over a period of time. Such data can be interpreted as a sequence …

Clustering Time Series Time Series Analysis

Ranking and significance of variable-length similarity-based time series motifs

2015-03-06 · Joan Serrà, Isabel Serra, Álvaro Corral, Josep Lluis Arcos

The detection of very similar patterns in a time series, commonly called motifs, has received continuous and increasing attention from diverse scientific communities. In particular, recent approaches for discovering simi…