Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links.

循环神经网络是一种动态系统，提供记忆功能以回忆过去的行为，这对于预测时间序列是必需的。在本文中，提出了一种受免疫算法启发的新型神经网络架构，并将其用于自然信号的预测，包括天气大数据信号。大数据分析是一个主要的研究领域，引起了学术界，工业界和政府的广泛关注，特别是在处理由于天气条件变化而引起的复杂动态问题方面。最近，物联网，传感器和环境情报系统的广泛部署导致气候领域数据呈指数增长。在这个研究中，我们集中运用免疫算法启发的动态自组织神经网络，对大天气数据进行分析。网络的学习策略使用受免疫算法启发的自组织隐藏层，着眼于信号的局部特性，而网络的循环链接旨在调出先前观察到的信号模式。与前馈多层神经网络和最新的递归网络（例如Elman和Jordan网络）相比，拟议的网络表现出更高的性能。我们的实验中使用了三种非线性和非平稳的天气信号。首先，将信号转换为平稳信号，然后进行5步提前预测。