A novel weighted approach for time series forecasting based on visibility graph

Abstract

Time series has attracted a lot of attention in many fields today. Time series forecasting algorithm based on complex network analysis is a research hotspot. How to use time series information to achieve more accurate forecasting is a problem. To solve this problem, this paper proposes a weighted network forecasting method to improve the forecasting accuracy. Firstly, the time series will be transformed into a complex network, and the similarity between nodes will be found. Then, the similarity will be used as a weight to make weighted forecasting on the predicted values produced by different nodes. Compared with the previous method, the proposed method is more accurate. In order to verify the effect of the proposed method, the experimental part is tested on M1, M3 datasets and Construction Cost Index (CCI) dataset, which shows that the proposed method has more accurate forecasting performance.

References in corpus (6)

• From time series to complex networks: the visibility graph
• Complex network approaches to nonlinear time series analysis
• Visibility graphs for image processing
• Analytical properties of horizontal visibility graphs in the Feigenbaum scenario
• Passive network evolution promotes group welfare in complex networks
• Efficient On-line Computation of Visibility Graphs