Results 71 to 80 of about 21,055,376 (292)

Quantile double AR time series models for financial returns

, 2013
We develop a novel quantile double autoregressive model for modelling financial time series. This is done by specifying a generalized lambda distribution to the quantile function of the location‐scale double autoregressive model developed by Ling (2004 ...
Cai, Y., Montes-Rojas, G., Olmo, J.
core   +1 more source

Uncovering long memory in high frequency UK futures [PDF]

, 2004
Accurate volatility modelling is paramount for optimal risk management practices. One stylized feature of financial volatility that impacts the modelling process is long memory explored in this paper for alternative risk measures, observed absolute and ...
Cotter, John
core   +2 more sources

Effects of Add‐On Icosapent Ethyl With Standard Treatment on Functional Outcomes and Inflammatory Biomarkers in Acute Ischemic Stroke: A Blinded Randomized Controlled Trial

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background Ischemic stroke, a major cause of mortality and long‐term disability, results from the abrupt cessation of cerebral blood flow due to vascular occlusion or rupture. Icosapent Ethyl (EPA‐EE), approved for hypertriglyceridemia, has anti‐inflammatory and antithrombotic properties that may lessen ischemic damage.
Mitra Mahmoudi Meymand, Seyed Hossein Aghamiri, Saeed Mohmammad Soleymani, Shadiyeh Bararpoor Poshkohi, Samaneh Masoudi, Hadi Esmaily   +5 more
wiley   +1 more source

DeepONet-Inspired Architecture for Efficient Financial Time Series Prediction

Mathematics
Financial time series prediction is a fundamental problem in investment and risk management. Deep learning models, such as multilayer perceptrons, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM), have been widely used in modeling ...
Zeeshan Ahmad, Shudi Bao, Meng Chen
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

microRNA‐7‐5p and α‐Synuclein SAA Predict Parkinson's Disease Phenoconversion

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Corroborate blood neuron‐derived extracellular vesicle (NDEV) alpha‐synuclein (αSyn), the CSF αSyn seed amplification assay (αSyn‐SAA), and blood microRNA‐7‐5p (miR‐7‐5p) as markers for Parkinson's disease (PD) phenoconversion and determine if combining these markers would help select subjects who would be more likely to phenoconvert.
Shayan Zadegan, Praveen Velammal, Kanmani Muthiah, Madhu Jatty, Christopher Adams   +4 more
wiley   +1 more source

Modelling Memory of Economic and Financial Time Series [PDF]

Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting.
Peter M Robinson
core  

Dynamic parameters for Brazilian financial time series

Economia Aplicada, 2002
In this study we analyse a Brazilian stock index called IBOVESPA using techniques from dynamical systems theory and stochastic processes. We discuss the Lyapunov exponent, the correlation dimension, the LempelZiv complexity, the Hurst exponent and the ...
Gerson Francisco, Cláudio Paiva, Rogerio Rosenfeld   +2 more
doaj  

AI-OPTIMIZED FUZZY CONVOLUTIONAL PREDICTION FOR FINANCIAL TIME SERIES

Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi
A deep convolutional prediction approach for financial time series is introduced in which fuzzy-cluster memberships and lagged observations are provided as inputs to a GA-tuned 1D convolutional network.
Furkan Keskin, Ozge Cagcag Yolcu, Sümeyye Polater   +2 more
doaj   +1 more source

On Multivariate Financial Time Series Classification

CoRR
This article investigates the use of Machine Learning and Deep Learning models in multivariate time series analysis within financial markets. It compares small and big data approaches, focusing on their distinct challenges and the benefits of scaling. Traditional methods such as SVMs are contrasted with modern architectures like ConvTimeNet.
openaire   +3 more sources