Results 61 to 70 of about 234,460 (307)
Multi-Attention Generative Adversarial Network for Multivariate Time Series Prediction
IEEE Access, 2021 Multivariate Time series data play important roles in our daily life. How to use these data in the process of prediction is a highly attractive study for many researchers.
Xiang Yin +5 more
doaj +1 more source
Arthritis Care &Research, EarlyView.Objective Clinical response to mycophenolic acid (MPA) is highly heterogeneous; thus, therapeutic drug level monitoring (TDM) may help improve treatment efficacy. This systematic review and meta‐analysis examined therapeutic ranges for MPA levels associated with better outcomes and safety in patients with systemic lupus erythematosus (SLE ...
Zahraa Qamhieh +5 more
wiley +1 more source
Estimator’s Properties of Specific Time-Dependent Multivariate Time Series
MathematicsThere is now a vast body of literature on ARMA and VARMA models with time-dependent or time-varying coefficients. A large part of it is based on local stationary processes using time rescaling and assumptions of regularity with respect to time.
Guy Mélard
doaj +1 more source
Clustering and Visualization of Multivariate Time Series
, 2010 The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS through unsupervised clustering and visualization.
Vellido Alcacena, Alfredo +1 more
openaire +2 more sources
Multivariate LSTM-FCNs for time series classification [PDF]
Neural Networks, 2019 Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully ...
Fazle Karim +3 more
openaire +3 more sources
Multimodal Data‐Driven Microstructure Characterization
Advanced Engineering Materials, EarlyView.A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang +4 more
wiley +1 more source
Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites
Advanced Functional Materials, EarlyView.A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou +5 more
wiley +1 more source
ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs
Machine Learning and Knowledge ExtractionLarge language models (LLMs) have recently demonstrated notable performance, particularly in addressing the challenge of extensive data requirements when training traditional forecasting models.
Jinglei Pei +5 more
doaj +1 more source
Greedy Gaussian segmentation of multivariate time series [PDF]
Advances in Data Analysis and Classification, 2018 We consider the problem of breaking a multivariate (vector) time series into segments over which the data is well explained as independent samples from a Gaussian distribution. We formulate this as a covariance-regularized maximum likelihood problem, which can be reduced to a combinatorial optimization problem of searching over the possible breakpoints,
David Hallac +2 more
openaire +2 more sources
Variance changes detection in multivariate time series [PDF]
, 2004 This paper studies the detection of step changes in the variances and in the correlation structure of the components of a vector of time series. Two procedures are considered.
Peña, Daniel +3 more
core