A novel model for chaotic complex time series with large of data forecasting
作者：
Highlights：
• A novel model is proposed for complex nonlinear time series forecasting.
• The model is more efficient to utilize due to its simple setting.
• The model does not require parameter training, but the prediction performance is stable.
• The S-means method based on K-means is developed for clustering and fusing similar data.
• The results show that the model is superior to some existing models.
摘要
•A novel model is proposed for complex nonlinear time series forecasting.•The model is more efficient to utilize due to its simple setting.•The model does not require parameter training, but the prediction performance is stable.•The S-means method based on K-means is developed for clustering and fusing similar data.•The results show that the model is superior to some existing models.
论文关键词：Time series forecasting,S-means,Time series with large of data,System identification
论文评审过程：Received 15 July 2020, Revised 28 March 2021, Accepted 29 March 2021, Available online 9 April 2021, Version of Record 15 April 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107009
