Time series have attracted widespread attention in many fields today. Based on the analysis of complex networks and visibility graph theory, a new time series forecasting method is proposed. In time series analysis, visibility graph theory transforms time series data into a network model. In the network model, the node similarity index is an important factor. On the basis of directly using the node prediction method with the largest similarity, the node similarity index is used as the weight coefficient to optimize the prediction algorithm. Compared with the single-node sampling node prediction algorithm, the multi-node sampling prediction algorithm can provide more accurate prediction values when the data set is sufficient. According to results of experiments on four real-world representative datasets, the method has more accurate forecasting ability and can provide more accurate forecasts in the field of time series and actual scenes.

翻译：根据对复杂网络和可见度图表理论的分析,提出了一个新的时间序列预测方法。在时间序列分析中,可见度图形理论将时间序列数据转换成网络模型。在网络模型中,节点相似指数是一个重要因素。在直接使用与最相似的节点预测方法的基础上,节点相似指数被用作优化预测算法的权重系数。与单节抽样节点预测算法相比,多节抽样预测算法可以在数据集充足时提供更准确的预测值。根据四个真实世界代表性数据集的实验结果,该方法具有更准确的预测能力,可以在时间序列和实际场景领域提供更准确的预报。