The objective of this paper is to extend a statistical approach to effectively provide look-ahead forecasts for cooling and electricity demand load. Our proposed model is a generalized form of a Cochrane–Orcutt estimation technique that combines a multiple linear regression model and a...

We discuss computational aspects of likelihood-based estimation of univariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.

We discuss computational aspects of likelihood-based estimation of unvariate ARFIMA (p,d,q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.