ABSTRACT Artificial Neural Networks (ANNs) models play an increasingly significant role in accurate time series prediction tools. However, an accurate time series forecasting using ANN requires an optimal model. Hence, great forecasting methods have been developed from optimized ANN models. Most of them focus more on input variables selection and preprocessing, topologies selection, optimum configuration and its associated parameters regardless of their input variables disposition. This paper provides an investigation of the effects of input variables disposition on ANNs models on training and forecasting performances. After investigation, a new ANNs optimization approach is proposed, consisting of finding optimal input variables disposition from the possible combinations. Therefore, a modified Back-Propagation neural networks training algorithm is presented in this paper. This proposed approach is applied to optimize the feed-forward and recurrent neural networks architectures; both built using traditional techniques, and pursuing to forecast the wind speed. Furthermore, the proposed approach is tested in a collaborative optimization method with single-objective optimization technique. Thus, Genetic Algorithm Back-Propagation neural networks aim to improve the forecasting accuracy relative to traditional methods was proposed. The experiment results demonstrate the requirement to take into consideration the input variables disposition to build a more optimal ANN model. They reveal that each proposed model is superior to its old considered model in terms of forecasting accuracy and thus show that the proposed optimization approach can be useful for time series forecasting accuracy improvement.

中文翻译：

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用于提高时间序列预测精度的人工神经网络模型的最佳输入变量配置

摘要 人工神经网络 (ANN) 模型在准确的时间序列预测工具中发挥着越来越重要的作用。但是，使用 ANN 进行准确的时间序列预测需要最佳模型。因此，已经从优化的 ANN 模型开发了很好的预测方法。他们中的大多数更关注输入变量的选择和预处理、拓扑选择、优化配置及其相关参数，而不管它们的输入变量配置如何。本文研究了输入变量配置对 ANN 模型对训练和预测性能的影响。经过调查，提出了一种新的人工神经网络优化方法，包括从可能的组合中寻找最佳输入变量配置。所以，本文提出了一种改进的反向传播神经网络训练算法。该方法用于优化前馈和循环神经网络架构；两者都使用传统技术建造，并追求预测风速。此外，所提出的方法在具有单目标优化技术的协同优化方法中进行了测试。因此，提出了遗传算法反向传播神经网络，旨在提高相对于传统方法的预测精度。实验结果表明需要考虑输入变量的配置以构建更优化的 ANN 模型。