Time series forecasting based on complex network in weighted node similarity

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-point sampling node prediction algorithm, the multi-point 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.