An efficient forecasting method for time series based on visibility graph and multi-subgraph similarity

Abstract

Recently network-based method for forecasting time series has become a hot research topic. Although some methods have been recognized for their prediction performance, how to mine more useful information of time series and make accuracy predictions is still an open question. To address this issue, we first propose a novel similarity measure called multi-subgraph similarity (Mss) for nodes in visibility graph. Then, a novel well-performed forecasting method for time series is proposed based on Mss. First, a time series is converted into a visibility graph. Afterward, the similarity distribution is obtained by Mss. Eventually, the prediction of time series is made using the similarity distribution. To demonstrate the proposed method is of better prediction performance, we compare the results of forecasting Construction Cost Index (CCI) and UCR data sets. The experiment results indicate that the proposed method could provide more accuracy predictions than compared methods. Moreover, the robustness test shows that the proposed method is of good robustness.