Results 51 to 60 of about 303,598 (207)
Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping
Atmosphere, 2022 Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns.
Fatin Nur Afiqah Suris +4 more
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Optimised meta-clustering approach for clustering Time Series Matrices [PDF]
, 2018 The prognostics (health state) of multiple components represented as time series data stored in vectors and matrices were processed and clustered more effectively and efficiently using the newly devised ‘Meta-Clustering’ approach.
Movahdisavehmotlagh, A.
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Fuzzy clustering of time series gene expression data with cubic-spline
, 2013 Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success.
Ali, Akhtar, Wang, Yu, Angelova, Maia
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Consistent Algorithms for Clustering Time Series. [PDF]
J. Mach. Learn. Res., 2016 The problem of clustering is considered for the case where every point is a time series. The time series are either given in one batch (offline setting), or they are allowed to grow with time and new time series can be added along the way (online setting).
Khaleghi, Azadeh +3 more
openaire +2 more sources
Clustering time series by linear dependency [PDF]
Statistics and Computing, 2018 We present a new way to find clusters in large vectors of time series by using a measure of similarity between two time series, the generalized cross correlation. This measure compares the determinant of the correlation matrix until some lag k of the bivariate vector with those of the two univariate time series.
Andrés M. Alonso, Daniel Peña
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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance. [PDF]
PLoS ONE, 2018 Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of ...
Yongli Liu +4 more
doaj +1 more source
Energies, 2021 The variability in generation introduced in the electrical system by an increasing share of renewable technologies must be addressed by balancing mechanisms, demand response being a prominent one.
Santiago Bañales +2 more
doaj +1 more source
Time-series clustering via quasi U-statistics
, 2015 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The problem of time-series discrimination and classification is discussed. We propose a novel clustering algorithm based on a
Pinheiro, A, Valk, M
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Hints of Earlier and Other Creation: Unsupervised Machine Learning in Financial Time-Series Analysis
Engineering Proceedings, 2023 This study extends previous work applying unsupervised machine learning to commodity markets. The first article in this sequence examined returns and volatility in commodity markets. The clustering of these time series supported the conventional ontology
James Ming Chen, Charalampos Agiropoulos
doaj +1 more source
, 2023 reservedTime series forecasting plays a pivotal role in various domains, such as finance, healthcare, and supply chain management. Traditional forecasting methods often assume that all time series follow a similar pattern, which may not hold true in real-
SARTORI, FRANCESCO
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