Results 211 to 220 of about 3,071 (290)

Impact of Uncertain Parameters on Navier–Stokes Equations With Heat Transfer via Polynomial Chaos Expansion

open access: yesInternational Journal for Numerical Methods in Fluids, EarlyView.
This study investigates the impact of uncertain parameters on Navier–Stokes equations coupled with heat transfer using the Intrusive Polynomial Chaos Method (IPCM). Sensitivity equations are formulated for key input parameters, such as viscosity and thermal diffusivity, and solved numerically using the Finite Element‐Volume method.
N. Nouaime   +3 more
wiley   +1 more source

Hawking's Singularity Theorem for Lipschitz Lorentzian Metrics. [PDF]

open access: yesCommun Math Phys
Calisti M   +4 more
europepmc   +1 more source

Scaling‐Aware Rating of Poisson‐Limited Demand Forecasts

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT Forecast quality should be assessed in the context of what is possible in theory and what is reasonable to expect in practice. Often, one can identify an approximate upper bound to a probabilistic forecast's sharpness, which sets a lower, not necessarily achievable, limit to error metrics.
Malte C. Tichy   +4 more
wiley   +1 more source

UK Forecasts of Annual GDP: Their Accuracy and the Information Categories Underlying Their Revisions

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT Policy makers are concerned with the accuracy of GDP forecasts and want to understand the reasons for the revision of forecasts. We study these issues by examining forecasts of annual UK GDP growth by a panel of agents, published monthly by HM Treasury. We focus on two main issues: the developing accuracy of the group‐mean forecast as horizons
Nigel Meade, Ciaran Driver
wiley   +1 more source

Forecasting New Employment Using Nonrepresentative Online Job Advertisements With an Application to the Italian and EU Labor Market

open access: yesJournal of Forecasting, EarlyView.
ABSTRACT Using online job advertisement data improves the timeliness and granularity depth of analysis in the labor market in domains not covered by official data. Specifically, its variation over time may be used as an anticipator of official employment variations.
Pietro Giorgio Lovaglio   +1 more
wiley   +1 more source

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

open access: yesJournal 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   +3 more
wiley   +1 more source

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