Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting
作者：
Highlights：
• Predicting the sequences decomposed by EEMD method separately.
• Considering the impact of complexity in the process of finding the nearest neighbors.
• Extending the traditional KNN method to the multi-dimension.
• Comparing the forecasting results with the original series and previous methods.
• Explore the impact of different types and complexity of data on prediction results.
摘要
•Predicting the sequences decomposed by EEMD method separately.•Considering the impact of complexity in the process of finding the nearest neighbors.•Extending the traditional KNN method to the multi-dimension.•Comparing the forecasting results with the original series and previous methods.•Explore the impact of different types and complexity of data on prediction results.
论文关键词：Multidimensional k-nearest neighbors (MKNN),Ensemble empirical mode decomposition (EEMD),Complexity measuring,EEMD–MKNN–TSPI,Forecasting
论文评审过程：Received 20 March 2020, Revised 24 November 2020, Accepted 2 December 2020, Available online 5 December 2020, Version of Record 10 December 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114443
