A comparative study on combinations of forecasts and their individual forecasts by means of simulated series

Aline Castello Branco Mancuso,Liane Werner

doi:10.4025/actascitechnol.v41i1.41452

Abstract

Over the years, several studies that compare individual forecasts with the combination of forecasts were published. There is, however, no unanimity in the conclusions. Furthermore, methods of combination by regression are poorly explored. This paper presents a comparative study of three methods of combination and their individual forecasts. Based on simulated data, it is evaluated the accuracy of Artificial Neural Networks, ARIMA and exponential smoothing models; calculating the combined forecasts through simple average, minimum variance and regression methods. Four accuracy measurements, MAE, MAPE, RMSE and Theil’s U, were used for choosing the most accurate method. The main contribution is the accuracy of the combination by regression methods.

Highlights

Forecasting is a process that comprises uncertainty; in order to minimize that uncertainty, there is a wide range of forecast techniques and models that include these different previsions, a method known as the combination of forecasts (Martins & Werner, 2012)
As this paper proposes a comparative study of accuracy in different forecast techniques, the comparison is performed directly with accuracy measurements
The method consists of the combination of different techniques aiming to take advantage of information from several individual forecasts, since prevision may be affected by several factors, each technique can offer a distinct contribution on the detected information (Clemen, 1989)

Summary

Introduction

Forecasting is a process that comprises uncertainty; in order to minimize that uncertainty, there is a wide range of forecast techniques and models that include these different previsions, a method known as the combination of forecasts (Martins & Werner, 2012). Since Bates and Granger (1969), combination techniques have been exhaustively compared. It is already possible to list several authors concluding that the combination of forecasts is more accurate than the best individual model (Bates & Granger, 1969; Clemen, 1989; Poncela, Rodríguez, Mangas, & Senra, 2011; Martins & Werner, 2012). Combination alone is not enough, it is necessary to know which techniques to use and how to combine them (Werner & Ribeiro, 2006). Nonlinear techniques of forecasting gained prominence in the most varied areas of knowledge, the forecast by Artificial Neural Networks was widely compared with other individual forecasts

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Results