A novel method for forecasting time series based on directed visibility graph and improved random walk

Yuntong Hu and Fuyuan Xiao

Physica A: Statistical Mechanics and its Applications, 2022, vol. 594, issue C

Abstract: Recently network-based method for forecasting time series has become a hot research topic. Although some proposed network-based methods achieve good performance in forecasting some series, how to mine more information of time series and make more accurate predictions is still an open question. To address this issue, we propose a novel reconstructing–forecasting method based on directed visibility graph and random walk process. Firstly, the observed time series is reconstructed to explore more information of series. Then, the reconstructed series is converted into a directed visibility graph. Afterwards, the reconstructed series is predicted with the similarity distribution obtained from improved random walk process. Eventually, the prediction of original time series is calculated using the predictions and the similarity distribution of the reconstructed one. To test the forecasting performance, the proposed method is applied to forecast construction cost index (CCI), China’s quarterly total GDP growth (GDP) and China’s tertiary industry quarterly GDP growth (TI). The results of experiments indicate that, with good robustness, the proposed method is of ability to provide more accurate predictions than compared methods.

Keywords: Directed visibility graph; Time series forecasting; Complex network; Random walk; Link prediction; Time series reconstructing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:594:y:2022:i:c:s0378437122000978

DOI: 10.1016/j.physa.2022.127029

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