English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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