基于可见图的时间序列加权预测新方法
A novel weighted approach for time series forecasting based on visibility graph
摘要：
时间序列在当今的许多领域都引起了人们的广泛关注。基于复杂网络分析的时间序列预测算法是一个研究热点。如何利用时间序列信息实现更准确的预测是一个难题。针对这一问题，提出了一种加权网络预测方法，以提高预测精度。首先将时间序列转化为复杂网络，找出节点之间的相似性，然后将相似性作为权值，对不同节点产生的预测值进行加权预测。与以往的方法相比，该方法具有更高的精度。为了验证所提方法的效果，实验部分在M1、M3数据集和建筑成本指数（CCI）数据集上进行了测试，结果表明所提方法具有更准确的预测性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
