Sep 11, 2023 · We propose Graph-Aware Contrasting for spatial consistency across MTS data. Specifically, we propose graph augmentations including node and edge augmentations.
Sep 11, 2023 · Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification.
Oct 6, 2024 · Bibliographic details on Graph Contextual Contrasting for Multivariate Time Series Classification.
Apr 21, 2024 · Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification.
MTS2Graph is an interpretable framework focusing on highly activated signal segments. We model the temporal dependencies of the segments via an evolution graph.
Missing: Contextual | Show results with:Contextual
Sep 18, 2024 · Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work.
Feb 9, 2024 · This approach improves the classification performance by learning the discriminative low-dimensional representations of multivariate time series.
People also ask
What is the best model for multivariate time series?
Can time series be multivariate?
What is the difference between univariate and multivariate time series analysis?
What is feature selection in multivariate time series?
In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series ...
Graph-Aware Contrasting for Multivariate Time-Series Classification [paper] ... Time-Series Representation Learning via Temporal and Contextual Contrasting ...
In contrast, our focus is on MTS data, where each sensor corresponds to an unattributed node, featuring only time- series signals. This distinction poses ...