Deep time-series clustering: A review
We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …
deep time-series clustering (DTSC), and a case study in the context of movement behavior …
Review of clustering methods for functional data
Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …
continuous functions underlying the discrete measurements/observations. Application of …
METRO: a generic graph neural network framework for multivariate time series forecasting
Multivariate time series forecasting has been drawing increasing attention due to its
prevalent applications. It has been commonly assumed that leveraging latent dependencies …
prevalent applications. It has been commonly assumed that leveraging latent dependencies …
Multiview unsupervised shapelet learning for multivariate time series clustering
N Zhang, S Sun - IEEE Transactions on Pattern Analysis and …, 2022 - ieeexplore.ieee.org
Multivariate time series clustering has become an important research topic in the time series
learning task, which aims to discover the correlation among multiple sequences and …
learning task, which aims to discover the correlation among multiple sequences and …
Unsupervised deep learning for IoT time series
Internet of Things (IoT) time-series analysis has found numerous applications in a wide
variety of areas, ranging from health informatics to network security. Nevertheless, the …
variety of areas, ranging from health informatics to network security. Nevertheless, the …
[HTML][HTML] Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study
Background: Depressive and manic episodes within bipolar disorder (BD) and major
depressive disorder (MDD) involve altered mood, sleep, and activity, alongside …
depressive disorder (MDD) involve altered mood, sleep, and activity, alongside …
An adaptive federated relevance framework for spatial temporal graph learning
Spatial–temporal data contains rich information and has been widely studied recently due to
the rapid development of relevant applications. For instance, medical institutions often use …
the rapid development of relevant applications. For instance, medical institutions often use …
[HTML][HTML] Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering
Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing
transportation systems, and improving public safety by providing useful insights into human …
transportation systems, and improving public safety by providing useful insights into human …
Feature weighting-based deep fuzzy C-means for clustering incomplete time series
Y Li, M Du, W Zhang, X Jiang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Time-series clustering is a crucial unsupervised technique for analyzing data, commonly
used in various fields, including medicine and stock analysis. However, in real-world …
used in various fields, including medicine and stock analysis. However, in real-world …
Contrastive shapelet learning for unsupervised multivariate time series representation learning
Recent studies have shown great promise in unsupervised representation learning (URL)
for multivariate time series, because URL has the capability in learning generalizable …
for multivariate time series, because URL has the capability in learning generalizable …