This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of...

Selection of appropriate climatic variables for prediction of electricity demand is critical as it affects the accuracy of the prediction. Different climatic variables may have different impacts on the electricity demand due to the varying geographical conditions. This paper uses...

This article provides a discussion of Clements and Galvão’s “Forecasting with Vector Autoregressive Models of Data Vintages: US output growth and inflation.” Clements and Galvão argue that a multiple-vintage VAR model can be useful for forecasting data that are subject to revisions....

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving...

Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation....

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential...

We assess the spot price forecasting performance of 10 commodity futures at various horizons up to two years and test whether this performance is affected by market conditions. We reject efficient markets based on in-sample tests but, out-of-sample, we find that the forecast from the futures...

The aim of this article is the accuracy evaluation of suitable models used for prediction of the unemployment rate development in Czech Republic under conditions of economic depression. Models were based on exponential smoothing and training of artificial neural networks. The most suitable...

The most common forecasting methods in business are based on exponential smoothing and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to...

Using monthly data for a set of variables, we examine the out-of-sample performance of various variance/covariance models and find that no model has consistently outperformed the others. We also show that it is possible to increase the probability mass toward the tails and to match reasonably...