Results 121 to 130 of about 18,151,738 (262)

Spectral Clustering of Precipitation Time Series in Golestan Province [PDF]

پژوهشنامه مدیریت حوزه آبخیز
Extended Abstract Background: Clustering time series of precipitation and other hydrological elements by direct use of classic methods, such as K-means, can be misleading because there is a time-lagged correlation in time series observations that is ...
Nader Jandaghi, Mojtaba Ghare Mahmoodlu, Majid Azimmohseni, Mahnaz Khalafi   +3 more
doaj  

Clustering of financial time series

, 2013
This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models.
D'URSO, Pierpaolo, Pierpaolo D’Urso, Carmela Cappelli, CAPPELLI, CARMELA, Riccardo Massari, Dario Di Lallo, MASSARI, Riccardo   +6 more
core   +1 more source

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

Advanced Engineering Materials, EarlyView.
Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar, Dhanadeep Dutta, G. Bharat Reddy   +2 more
wiley   +1 more source

Forecasting Time Series from Clusters. [PDF]

Forecasting large numbers of time series is a costly and time-consuming exercise. Before forecasting a large number of series that are logically connected in some way, the authors can first cluster them into groups of similar series.
Inder, B., Marahaj, E.A.
core  

PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science

Advanced Engineering Materials, EarlyView.
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley   +1 more source

Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform. [PDF]

J Classif, 2023
Kim M, Oh HS, Lim Y.
europepmc   +1 more source

OntOMat: Toward Ontology‐Based Product and Process Design Engineering and Optimization Solutions Fueling Circular Value Chains

Advanced Engineering Materials, EarlyView.
The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ, Jörg Hohe, Martin Kördel, Michael Giese, Niklas Toffert, Christian Seidel, Olaf Rehme, Patrik Schneider, Oliver Focke, Elie Abi Aoun, Claas Falldorf, Beñat Gutierrez, Alexandra Kühn, Sven Torstrick-von der Lieth, Christoph Herden, Erik Johannsen, Jan‐Uwe R. Schmidt, Felix Winkelmann, Andreas Schott, Daniel Schott   +19 more
wiley   +1 more source

KDiscShapeNet: A Structure-Aware Time Series Clustering Model with Supervised Contrastive Learning

Mathematics
Time series clustering plays a vital role in various analytical and pattern recognition tasks by partitioning structurally similar sequences into semantically coherent groups, thereby facilitating downstream analysis.
Xi Chen, Yufan Jiang, Yingming Zhang, Chunhe Song   +3 more
doaj   +1 more source

Comparison of time series clustering methods for identifying novel subphenotypes of patients with infection. [PDF]

J Am Med Inform Assoc, 2023
Bhavani SV, Xiong L, Pius A, Semler M, Qian ET, Verhoef PA, Robichaux C, Coopersmith CM, Churpek MM.   +8 more
europepmc   +1 more source

Unfolding preprocessing for meaningful time series clustering.

, 2006
Clustering methods are commonly applied to time series, either as a preprocessing stage for other methods or in their own right. In this paper it is explained why time series clustering may sometimes be considered as meaningless.
Simon, Geoffroy, Verleysen, Michel, Lee, John Aldo   +2 more
core   +1 more source