Combination of forecasts: A bibliography (original) (raw)

Combining Forecasting Procedures: Some Theoretical Results

2000

We study some methods of combining procedures for forecasting a continuous random variable. Statistical risk bounds under the square error loss are obtained under mild distributional assumptions on the future given the current outside information and the past observations. The risk bounds show that the combined forecast automatically achieves the best performance among the candidate procedures up to a constant factor and an additive penalty term. In term of the rate of convergence, the combined forecast performs as well as if one knew which candidate forecasting procedure is the best in advance.

To combine or not to combine? issues of combining forecasts

Journal of Forecasting, 1992

ABSTRACT This paper addresses issues such as: Does it always pay to combine individual forecasts of a variable? Should one combine an unbiased forecast with one that is heavily biased? Should one use optimal weights as suggested by Bates and Granger over twenty ...

To combine or not to combine: selecting among forecasts and their combinations

International Journal of Forecasting, 2005

Much research shows that combining forecasts improves accuracy relative to individual forecasts. In this paper we present experiments, using the 3003 series of the M3-competition, that challenge this belief: on average across the series, the best individual forecasts, based on post-sample performance, perform as well as the best combinations. However, this finding lacks practical value since it requires that we identify the best individual forecast or combination using post sample data. So we propose a simple model-selection criterion to select among forecasts, and we show that, using this criterion, the accuracy of the selected combinations is significantly better and less variable than that of the selected individual forecasts. These results indicate that the advantage of combining forecasts is not that the best possible combinations perform better than the best possible individual forecasts, but that it is less risky in practice to combine forecasts than to select an individual forecasting method.

Combining and Evaluating Probabilistic Forecasts

2009

In presenting this dissertation in partial fulfillment of the requirements for the doctoral degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of the dissertation is allowable only ...

Forecast Combination

2020

Actualmente existen diversas metodologías de pronóstico, que van desde el conocimiento empírico hasta métodos innovadores, individuales o combinados, que demuestran resultados óptimos. Este documento se deriva de un proceso de investigación y presenta alternativas relacionadas con las combinaciones de pronósticos, utilizando metaheurísticas, por ejemplo, mediante la búsqueda tabú y la programación evolutiva para optimizar el pronóstico. El documento presenta pronósticos combinados basados en la programación evolutiva utilizando mezclas de modelos de regresión bayesiana y modelos de regresión lineal clásico, el modelo de media móvil integrado autorregresivo, el suavizado exponencial y la regresión bayesiana. El documento presenta dos artículos derivados de investigación, la primera compara el algoritmo combinado con los resultados individuales de estos modelos individuales y con la combinación de Bates y Granger utilizando un indicador de error y el valor simétrico de error absoluto ...

Combining forecasts: The end of the beginning or the beginning of the end

International Journal of Forecasting, 1989

Research from over 200 studies demonstrates that combining forecasts produces consistent but modest gains in accuracy. However, this research does not define well the conditions under which combining is most effective nor how methods should be combined in each situation. Rule-based forecasting can be used to define these conditions and to specify more effective combinations.

A Data-Weighted Prior Estimator for Forecast Combination

Entropy, 2019

Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good forecasters, even in the situation of little information. With this purpose, the data-weighted prior (DWP) estimator proposed by Golan (2001) is used for forecaster selection and simultaneous parameter estimation in linear statistical models. In particular, we examine the ability of the DWP estimator to effectively select relevant forecasts among all forecasts. We test the accuracy of the proposed...