.png){kind=icon}
返回
Why do EMD-based methods improve prediction? A multiscale complexity perspective
.png){kind=icon}
PRE
AI
DOI：10.1002/for.2593.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Empirical mode decomposition (EMD)-based ensemble methods have become increasingly popular in the research field of forecasting, substantially enhancing prediction accuracy. The key factor in this type of method is the multiscale decomposition that immensely mitigates modeling complexity. Accordingly, this study probes this factor and makes further innovations from a new perspective of multiscale complexity. In particular, this study quantitatively investigates the relationship between the decomposition performance and prediction accuracy, thereby developing (1) a novel multiscale complexity measurement (for evaluating multiscale decomposition), (2) a novel optimized EMD (OEMD) (considering multiscale complexity), and (3) a novel OEMD-based forecasting methodology (using the proposed OEMD in multiscale analysis). With crude oil and natural gas prices as samples, the empirical study statistically indicates that the forecasting capability of EMD-based methods is highly reliant on the decomposition performance; accordingly, the proposed OEMD-based methods considering multiscale complexity significantly outperform the benchmarks based on typical EMDs in prediction accuracy.
Keyword:
empirical mode decomposition
energy forecasting
entropy
multiscale complexity
time series forecasting
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
2.3K
被引数:
3.0K
机构
引用论文
A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting一种用于核能消耗预测的新型混合集成学习范式.png)
APPLIED ENERGY
APPLIED ENERGYIF11
PREAI
分享收藏The behaviour mechanism analysis of regional natural gas prices: A multi-scale perspectiveENERGY
ENERGYIF9.4
PREAI
分享收藏Forecasting crude oil price with an EMD-based neural network ensemble learning paradigmENERGY ECONOMICS
ENERGY ECONOMICSIF14.2
PREAI
分享收藏
A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecastingENERGY
ENERGYIF9.4
PREAI
分享收藏
查看更多内容