Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.

时间序列在无线物联网中具有广泛的用途。本文在AIC优化的减法聚类算法（AIC-DSCA-SSNET，AD-SSNET）预测非线性和不稳定的时间序列之后，提出了一种基于小世界无标度网络的非线性时间序列预测算法，从而改善了预测准确性。引入AD-SSNET作为基于回波状态网络的存储库，以提高非线性时间序列的预测能力，并结合人工智能方法构建预测模型训练样本。首先，通过AIC-DSCA自适应地获得网络中随机分布的神经元的最佳聚类方案，然后根据集群内优先级连接算法构建AD-SSNET。最后，根据突触信息计算储层突触矩阵。实验结果表明，提出的非线性时间序列预测算法扩展了油藏谱半径的可行范围，提高了非线性时间序列的预测精度，对无线物联网时代的时间序列分析具有重要意义。