关于将机器学习用于重要波高时间序列预测的论文的评论:复杂模型并不优于自回归模型

IF 3.1 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Ocean Modelling Pub Date : 2024-03-27 DOI:10.1016/j.ocemod.2024.102364
Haoyu Jiang , Yuan Zhang , Chengcheng Qian , Xuan Wang
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引用次数: 0

摘要

显著波高(SWH)在海洋工程的许多方面都至关重要。因此,准确预测 SWH 具有巨大的实用价值。最近,人工智能(AI)时间序列预测方法被广泛用于单点短期 SWH 时间序列预测,许多基于人工智能的模型都声称取得了良好的效果。然而,这些复杂的人工智能模型究竟能在多大程度上超越传统方法,却在很大程度上被忽视了。本研究比较了自动回归(AR)、极梯度提升(XGB)、人工神经网络(ANN)、长短期记忆(LSTM)和波网(WaveNet)等五种不同模型在 16 个浮标位置的 SWH 时间序列预测中的表现。令人惊讶的是,结果表明不同模型之间的性能差异微乎其微,这表明所有这些人工智能模型都只是从数据中 "学习 "了线性自回归。此外,我们注意到最近的许多研究都使用信号分解法进行此类时间序列预测,而且大多数研究都对测试集进行了分解,这是错误的。
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Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression

Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only “learned” the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG.

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来源期刊
Ocean Modelling
Ocean Modelling 地学-海洋学
CiteScore
5.50
自引率
9.40%
发文量
86
审稿时长
19.6 weeks
期刊介绍: The main objective of Ocean Modelling is to provide rapid communication between those interested in ocean modelling, whether through direct observation, or through analytical, numerical or laboratory models, and including interactions between physical and biogeochemical or biological phenomena. Because of the intimate links between ocean and atmosphere, involvement of scientists interested in influences of either medium on the other is welcome. The journal has a wide scope and includes ocean-atmosphere interaction in various forms as well as pure ocean results. In addition to primary peer-reviewed papers, the journal provides review papers, preliminary communications, and discussions.
期刊最新文献
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