Results 11 to 20 of about 10,612,437 (315)
Exploring Dynamic Structures in Matrix-Valued Time Series via Principal Component Analysis
Axioms, 2023 Time-series data are widespread and have inspired numerous research works in machine learning and data analysis fields for the classification and clustering of temporal data. While there are several clustering methods for univariate time series and a few
Lynne Billard +2 more
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [PDF]
International Conference on Learning Representations, 2023 Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for ...
Ming Jin +10 more
semanticscholar +1 more source
Time Series Modelling With Semiparametric Factor Dynamics [PDF]
Journal of the American Statistical Association, 2007 High-dimensional regression problems which reveal dynamic behavior are typically analyzed by time propagation of a few number of factors. The inference on the whole system is then based on the low-dimensional time series analysis. Such highdimensional problems occur frequently in many different fields of science. In this paper we address the problem of
Park, Byeong U. +3 more
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Analysis of the development of Oil and Gas Industry in present conditions
Статистика и экономика, 2019 Purpose of the study. This study examines the state of companies of oil sector based on the analysis of dynamics and relationship between basic financial indicators, characterizing the activities of oil companies; it identifies factors affecting the ...
E. I. Larionova +2 more
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Dynamic Matrix Clustering Method for Time Series Events
Jisuanji kexue yu tansuo, 2021 Time series events clustering is the basis of studying the classification of events and mining analysis. Most of the existing clustering methods directly aim at continuous events with time attribute and complex structure, but the transformation of ...
MA Ruiqiang, SONG Baoyan, DING Linlin, WANG Junlu
doaj +1 more source
Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data [PDF]
Journal of Cognitive Neuroscience, 2016 Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to ...
Tijl Grootswagers, S. Wardle, T. Carlson
semanticscholar +1 more source
Dynamic MRI in a COVID-19 patient: a case series [PDF]
Digital Diagnostics, 2023 Extensive spread of the coronavirus disease (COVID-19) prompted an investigation of its diagnostic features. Acute viral pneumonia associated with COVID-19 has been described in detail using CT, radiography, and MRI. There is no data in the literature on
Yuriy A. Vasilev +6 more
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Dynamic scaling approach to study time series fluctuations [PDF]
, 2007 We propose a new approach for properly analyzing stochastic time series by mapping the dynamics of time series fluctuations onto a suitable nonequilibrium surface-growth problem. In this framework, the fluctuation sampling time interval plays the role of
A. S. Weigend +9 more
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IEEE Access, 2020 Time series segmentation is an important vehicle of data mining and extensively applied in the areas of machine learning and anomaly detection. In real world tasks, dynamics widely exist in time series but have been little concerned.
Shaowen Lu, Shuyu Huang
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Dynamic Model for LES Without Test Filtering: Quantifying the Accuracy of Taylor Series Approximations [PDF]
, 2001 The dynamic model for large-eddy simulation (LES) of turbulent flows requires test filtering the resolved velocity fields in order to determine model coefficients.
Charlette, Fabrice +2 more
core +2 more sources