Otimização da função de fitness para a evolução de redes neurais com o uso de análise envoltória de dados aplicada à previsão de séries temporais

Abstract

The techniques for Time Series Analysis and Forecasting have great presence on the literature over the years. The computational resources combined with statistical techniques are improving the predictive results, and these results have been become increasingly accurate. Computational methods base on Artificial Neural Networks (ANN) and Evolutionary Computing (EC) are presenting a new approach to solve the Time Series Analysis and Forecasting problem. These computational methods are contained in the branch of Artificial Intelligence (AI), and they are biologically inspired, where the ANN models are based on the neural structure of intelligent organism, and the EC uses the concept of nature selection of Charles Darwin. Both methods acquire experience from prior knowledge and example of the given problem. In particular, for the Time Series Forecasting Problem, the objective is to find the predictive model with highest forecast perfomance, where the performance measure are statistical errors. However, there is no universal criterion to identify the best performance measure. Since the ANNs are the predictive models, the EC will constantly evaluate the forecast performance of the ANNs, using a fitness functions to guide the predictive model for an optimal solution. The Data Envelopment Analysis (DEA) was employed to predictive determine the best combination of variables based on the relative efficiency of the best models. Therefore, this work to study the optimization Fitness Function process with Data Envelopment Analysis applied the Intelligence Hybrid System for time series forecasting problem. The data analyzed are composed by financial data series, agribusiness and natural phenomena. The C language program was employed for implementation of the hybrid intelligent system and the R Environment version 2.12 for analysis of DEA models. In general, the perspective of using DEA procedure to evaluate the fitness functions were satisfactory and serves as an additional resource in the branch of time series forecasting. Researchers need to compute the results under different perspectives, whether in the matter of the computational cost of implementing a particular function or which function was more efficient in the aspect of assessing which combinations are unwanted saving time and resources.