This paper uses forecast combination methods to forecast output growth in a seven-country quarterly economic data set covering 1959–1999, with up to 73 predictors per country. Although the forecasts based on individual predictors are unstable over time and across countries, and on average perform worse than an autoregressive benchmark, the combination forecasts often improve upon autoregressive...

The THORPEX goal of improving weather forecasts from one day to two weeks suggests the combination of multi-model and multi-initial-condition ensembles of simulations into a probabilistic forecast of some kind. This contribution presents a simple methodology for combining forecasts (be they high resolution or ensemble forecasts) into a predictive distribution function of a chosen target variabl...

Efficient integration of renewable energy sources into the electricity grid has become one of the challenging problems in recent years. This issue is more critical especially for unstable energy sources such as wind. The focus of this work is the performance analysis of several alternative wind forecast combination models in comparison to the current forecast combination module of the wind powe...

This paper provides a description and experimental comparison of different forecast combination techniques for the application of Revenue Management forecasting for Airlines. In order to benefit from the advantages of forecasts predicting seasonal demand using different forecast models on different aggregation levels and to reduce the risks of high noise terms on low level predictions and overg...

In the paper, two pre-processing methods for load forecast sampling data including multiwavelet transformation and chaotic time series are introduced. In addition, multi neural network for load forecast including BP artificial neural network, RBF neural network and wavelet neural network are introduced, too. Then, a combination load forecasting model for power load based on chaotic time series,...

SAS® High-Performance Forecasting 4.1 offers a new, innovative process for automatically combining forecasts. Forecast combination, also called ensemble forecasting, is the subject of many academic papers in statistical and forecasting journals; it is a known technique for improving forecast accuracy and reducing variability of the resulting forecasts. By integrating these methods into a single...