Results 51 to 60 of about 211,599 (259)

Multivariate Financial Time-Series Prediction With Certified Robustness

IEEE Access, 2020
The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention.
Hui Li, Yunpeng Cui, Shuo Wang, Juan Liu, Jinyuan Qin, Yilin Yang   +5 more
doaj   +1 more source

A Convolutional Transformer Model for Multivariate Time Series Prediction

IEEE Access, 2022
This paper presents a multivariate time series prediction framework based on a transformer model consisting of convolutional neural networks (CNNs).
Dong-Keon Kim, Kwangsu Kim
doaj   +1 more source

Integrating Time‐Adjusted Imaging Instability Into Functional Outcome Prediction After Intracerebral Hemorrhage: Development and Validation of the HAGIV Score

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Early risk stratification may support clinical decision‐making in spontaneous intracerebral hemorrhage (ICH). We aimed to develop and internally validate HAGIV, a score integrating frequency of imaging markers (FIM), a time‐adjusted non‐contrast computed tomography (CT) metric of hematoma expansion, with established predictors for 90‐
Lei Song, Ren Ke, Anqi Chen, Yufei Fu, Hang Zhou, Rujia Wang, Liwei Zou, Yongqiang Yu, Ming Yuan, Xiaoming Qiu, Mengzhou Xue   +10 more
wiley   +1 more source

KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network [PDF]

Jisuanji kexue
Time series data is widely present in fields such as finance,healthcare,industry,and transportation.Time Series Ano-maly Detection(TSAD) is crucial for ensuring system stability and safety.Most current time series anomaly detection methods are ...
WANG Cheng, JIN Cheng
doaj   +1 more source

Clinical Significance of Therapeutic Drug Level Monitoring for Mycophenolate in Patients With Extrarenal Systemic Lupus Erythematosus—A Systematic Review and Meta‐Analysis

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, Dalia Sriwi, Callie Saric, Tripti Singh, Christie M. Bartels, Shivani Garg   +5 more
wiley   +1 more source

Estimator’s Properties of Specific Time-Dependent Multivariate Time Series

Mathematics
There 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

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, Peter Nystrup, Stephen P. Boyd   +2 more
openaire   +2 more sources

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga   +2 more
wiley   +1 more source

STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

Tsinghua Science and Technology
Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields.
Zhuolun Jiang, Zefei Ning, Hao Miao, Li Wang   +3 more
doaj   +1 more source

Forecasting Multivariate Time Series with the Theta Method [PDF]

Journal of Forecasting, 2015
AbstractIn this study building on earlier work on the properties and performance of the univariate Theta method for a unit root data‐generating process we: (a) derive new theoretical formulations for the application of the method on multivariate time series; (b) investigate the conditions for which the multivariate Theta method is expected to forecast ...
Dimitrios D. Thomakos, Konstantinos Nikolopoulos   +1 more
openaire   +1 more source