TIME SERIES FORECASTING. A COMPARATIVE STUDY BETWEEN AN EVOLVING ARTIFICIAL NEURAL NETWORKS SYSTEM AND STATISTICAL METHODS

被引:14
|
作者
Peralta Donate, Juan [1 ]
Gutierrez Sanchez, German [1 ]
Sanchis De Miguel, Araceli [1 ]
机构
[1] Univ Carlos III Madrid, Dept Comp Sci, Av Univ 30, Madrid 28911, Spain
关键词
Evolutionary computation; genetic algorithms; artificial neural networks; time series; forecasting; statistic;
D O I
10.1142/S0218213011000462
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Accurate time series forecasting are important for displaying the manner in which the past continues to affect the future and for planning our day to-day activities. In recent years, a large literature has evolved on the use of evolving artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified non-linear relationship between time series variables. In this work, a new approach of a previous Automatic Design of Artificial Neural Networks (ADANN) system applied to forecast time series is tackled. The automatic process to design artificial neural networks is carried out by a genetic algorithm (GA). These new methods, in order to get an accurate forecasting, are related with: shuffling training and validation patterns obtained from time series values and trying to improve the fitness function used in the global learning process (i.e. GA) using a new patterns set called validation II apart of the two used till the moment (i.e. training and validation). The object of this study is to try to improve the final forecasting getting an accurate system. In this paper, we also compare the forecasting ability of the ARIMA approach, evolving artificial neural networks (ADANN), unobserved components model (UCM) and a forecasting tool called Forecast Pro software using six benchmark time series.
引用
收藏
页数:26
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