effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting …), NBEATSx-VP demonstrates a minimum 9% improvement in forecast precision compared to benchmark models. Considering Quasi …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or …

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous …

learning, dimensionality reduction, forecast combination and amalgamation approaches. Our results highlight the predictive …

specifically, we first use the current state-of-the-art frameworks to forecast monthly oil prices and subsequently we use these …

care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the …, shrinkage and forecast combinations …

are robust to a series of specifications. We believe that this is the first attempt to combine GARCH models to forecast …