Results 111 to 120 of about 262,580 (281)

Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices

Journal of Forecasting, EarlyView.
ABSTRACT This paper presents an evaluation of the accuracy of machine learning (ML) techniques in forecasting the realized volatility of West Texas Intermediate (WTI) crude oil prices. We compare several ML algorithms, including regularization, regression trees, random forests, and neural networks, to several heterogeneous autoregressive (HAR) models ...
Talha Omer, Kristofer Månsson, Pär Sjölander, Gazi Salah Uddin   +3 more
wiley   +1 more source

Subspace Learning for Dual High-Order Graph Learning Based on Boolean Weight

Entropy
Subspace learning has achieved promising performance as a key technique for unsupervised feature selection. The strength of subspace learning lies in its ability to identify a representative subspace encompassing a cluster of features that are capable of
Yilong Wei, Jinlin Ma, Ziping Ma, Yulei Huang   +3 more
doaj   +1 more source

Masked Subspace Clustering Methods

To further utilize the unsupervised features and pairwise information, we propose a general Bilevel Clustering Optimization (BCO) framework to improve the performance of clustering. And then we introduce three special cases on subspace clustering with two different types of masks.
Jiebo Song, Huaming Ling
openaire   +2 more sources

Quadratic Hedging of American Options Under GARCH Models

Journal of Futures Markets, EarlyView.
ABSTRACT American options are widely traded in financial markets, yet there is a scarcity of literature on hedging in incomplete markets. In this paper, we derive optimal hedging ratios and option values using Local Risk Minimization (LRM) and Global Risk Minimization (GRM) hedging strategies through dynamic programming.
Junmei Ma, Chen Wang, Wei Xu
wiley   +1 more source

Progress of metabolomics‐centric multi‐omics research in medicine

iMetaOmics, EarlyView.
The graphical abstract illustrates a holistic roadmap for metabolomics‐centric multi‐omics integration in medical research. The upper panel depicts the technological transition from traditional bulk analysis to high‐resolution single‐cell and spatial methodologies, specifically addressing inherent challenges such as molecular complexity and dynamic ...
Ziyi Wang, Shiwen Deng, Xiaoxu Wang, Xiao Chang, Na Guo, Peng Chen, Hongjun Yang   +6 more
wiley   +1 more source

On data preprocessing for subspace methods

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
In modern data analysis often the first step is to perform some data preprocessing, e.g. detrending or elimination of periodic components of known period length. This is normally done using least squares regression. Only afterwards black box models are estimated using either pseudo-maximum-likelihood methods, prediction error methods or subspace ...
openaire   +4 more sources

Reduced occurrence of alpha waves during resting state predicts high attention‐deficit/hyperactivity disorder traits in young adults

JCPP Advances, EarlyView.
Abstract Background Attention‐deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition with significant cognitive and social impacts. Identifying reliable biomarkers for ADHD is crucial for developing personalised therapies. Electroencephalography (EEG) alpha oscillations (8–12 Hz) have been suggested as a potential biomarker, but ...
Julio Rodriguez‐Larios, Ümit Aydin, Gráinne McLoughlin   +2 more
wiley   +1 more source

Edge‐Length Preserving Embeddings of Graphs Between Normed Spaces

Journal of Graph Theory, EarlyView.
ABSTRACT The concept of graph embeddability, initially formalized by Belk and Connelly and later expanded by Sitharam and Willoughby, extends the question of embedding finite metric spaces into a given normed space. A finite simple graph G = ( V , E ) $G=(V,E)$ is said to be ( X , Y ) $(X,Y)$‐embeddable if any set of induced edge lengths from an ...
Sean Dewar, Eleftherios Kastis, Derek Kitson, William Sims   +3 more
wiley   +1 more source

Elevator dynamic monitoring and early warning system based on machine learning algorithm

IET Networks, EarlyView., 2022
In order to monitor and warn the elevator dynamics, in this work, the machine learning algorithm is introduced, and the particle swarm algorithm is used to perfect the model. The model is optimised, and the experimental comparison shows that the optimisation of the model parameters can further improve the accuracy of the elevator load prediction. Then,
Shuai Zhang, Qiangguo Yin, Jinlong Wang
wiley   +1 more source

Clinical Feasibility of Deep Learning Contrast Synthesis From MR Fingerprinting in Knee Osteoarthritis

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background Magnetic Resonance Fingerprinting (MRF) enables rapid quantitative parameter mapping from which synthetic clinical contrast images can be derived using deep learning (DL). Purpose This study evaluates the reliability and interchangeability of MRF‐derived synthetic knee MRI relative to conventional MRI in patients with osteoarthritis.
Mika T. Nevalainen, Olli Nykänen, Jyri Järvinen, Antti Kemppainen, Lasse Räsänen, Victor Casula, Martijn Cloos, Riccardo Lattanzi, Mikko J. Nissi, Miika T. Nieminen   +9 more
wiley   +1 more source