Results 171 to 180 of about 40,912 (303)

A Deep Learning Framework for Forecasting Medium‐Term Covariance in Multiasset Portfolios

Journal of Forecasting, Volume 45, Issue 4, Page 1797-1828, July 2026.
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

Application of seasonal-adjusted hybrid models for forecasting Discomfort Index in a heat-prone region of Bangladesh. [PDF]

PLoS One
Binte Ahmed A, Uddin Qureshi MM, Hussain Khan MM, Dulmini A, Haque Mollah MA, Rois R.   +5 more
europepmc   +1 more source

Coherent Forecasting of Realized Volatility

Journal of Forecasting, Volume 45, Issue 4, Page 1714-1729, July 2026.
ABSTRACT The QLIKE loss function is the stylized favorite of the literature on volatility forecasting when it comes to out‐of‐sample evaluation and the state of the art model for realized volatility (RV) forecasting is the HAR model, which minimizes the squared error loss for in‐sample estimation of the parameters.
Marius Puke, Karsten Schweikert
wiley   +1 more source

Investigating the impact of investor attention on AI-based stocks: A comprehensive analysis using quantile regression, GARCH, and ARIMA models. [PDF]

PLoS One
Ravichandran S, Afjal M.
europepmc   +1 more source

Electricity Price Prediction Using Multikernel Gaussian Process Regression Combined With Kernel‐Based Support Vector Regression

Journal of Forecasting, Volume 45, Issue 4, Page 2059-2077, July 2026.
ABSTRACT This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian process regression (GPR) and support vector regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out‐of‐sample data is not ...
Abhinav Das, Stephan Schlüter, Lorenz Schneider   +2 more
wiley   +1 more source

Hybrid time series and machine learning models for forecasting cardiovascular mortality in India: an age specific analysis. [PDF]

BMC Public Health
Teja MD, Rayalu GM.
europepmc   +1 more source

Forecasting Natural Gas Futures Price Volatility of the United States: National Versus State‐Level Climate Concern Indexes

Journal of Futures Markets, Volume 46, Issue 7, Page 1275-1297, July 2026.
ABSTRACT This paper uses GARCH‐MIDAS to predict US natural gas futures volatility using national and state‐level Climate Concern Indexes (CCIs). We find that both national and state‐level CCIs positively affect price volatility. Notably, models using state‐level data—specifically those utilizing least‐squares (LS) weighting combinations—surpass the ...
Afees A. Salisu, Ahamuefula E. Ogbonna, Rangan Gupta, Onur Polat   +3 more
wiley   +1 more source

Enhancing Prediction by Incorporating Entropy Loss in Volatility Forecasting. [PDF]

Entropy (Basel)
Urniezius R, Petrauskas R, Vaitkus V, Karimov J, Brazauskas K, Repsyte J, Kacerauskiene E, Harms T, Dargiene J, Ezerskis D.   +9 more
europepmc   +1 more source

AlphaFold2‐Guided Cyclic Peptide Stabilizer Design to Target Protein–Protein Interactions

Proteins: Structure, Function, and Bioinformatics, Volume 94, Issue 7, Page 1356-1370, July 2026.
ABSTRACT The control and modulation of protein–protein interactions (PPIs) is of central importance for the majority of biological processes and most biomedical applications. Stabilization of PPIs, besides inhibition, is of growing pharmaceutical interest.
Niklas Halbwedl, Martin Zacharias
wiley   +1 more source

FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty. [PDF]

Sci Rep
Wang TW, Shaikh ZA, Yong SL, Elmannai H, Por LY.   +4 more
europepmc   +1 more source