Machine Learning Approaches to Forecast the Realized Volatility of Crude Oil Prices
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
Line Bound States in the Continuum (BIC)s Code
Cerjan, Benjamin, Cerjan, Alexander
openalex +1 more source
Correction: G-bic: generating synthetic benchmarks for biclustering [PDF]
Eduardo N. Castanho +3 more
openalex +1 more source
A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios
ABSTRACT Forecasting the covariance matrix of asset returns is central to portfolio construction, risk management, and asset pricing. However, most existing models struggle at medium‐term horizons, several weeks to months, where shifting market regimes and slower dynamics prevail.
Pedro Reis, Ana Paula Serra, João Gama
wiley +1 more source
Switch to Fixed Dose of Doravirine, Lamivudine, Tenofovir Disoproxil Fumarate Versus Bictegravir, Emtricitabine, and Tenofovir Alafenamide Fumarate in Virologically Suppressed Adults on Efavirenz-Based Regimens: 48-Week Results of a Real-world, Prospective, Observational Cohort Study. [PDF]
Li A +12 more
europepmc +1 more source
Forecasting Count Data With Varying Dispersion: A Latent‐Variable Approach
ABSTRACT Count data, such as product sales and disease case counts, are common in business forecasting and many areas of science. Although the Poisson distribution is the best known model for such data, its use is severely limited by its assumption that the dispersion is a fixed function of the mean, which rarely holds in real‐world scenarios.
Easton Huch +3 more
wiley +1 more source
Harnessing optical bound states in the continuum for ultrafast, reconfigurable, long-range photonic networks. [PDF]
Ma J, Yu Y, Liu J.
europepmc +1 more source

