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

TL;DR

This paper introduces a weighted complex network approach for time series forecasting, transforming data into networks to leverage node similarities for improved prediction accuracy, validated on multiple datasets.

Contribution

It proposes a novel weighted network method that enhances forecasting accuracy by utilizing node similarities in complex networks derived from time series.

Findings

The method outperforms previous approaches in accuracy.

Experimental results on M1, M3, and CCI datasets confirm improved performance.

Weighted network approach is effective for diverse datasets.

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.