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Forecasting Nonstationary Time Series Based on Hilbert-Huang Transform and Machine Learning

机译:基于希尔伯特-黄变换和机器学习的非平稳时间序列预测

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摘要

We propose a modification of the adaptive approach to time series forecasting. On the first stage, the original signal is decomposed with respect to a special empirical adaptive orthogonal basis, and the Hilbert's integral transform is applied. On the second stage, the resulting orthogonal functions and their instantaneous amplitudes are used as input variables for the machine learning unit that employs a hybrid genetic algorithm to train an artificial neural network and a regressive model based on support vector machines. The efficiency of the proposed approach is demonstrated on real data coming from Nord Pool Spot and Australian National Energy Market.
机译:我们建议对时间序列预测的自适应方法进行修改。在第一阶段,相对于特殊的经验自适应正交基分解原始信号,并应用希尔伯特积分变换。在第二阶段,将所得的正交函数及其瞬时振幅用作机器学习单元的输入变量,该机器学习单元采用混合遗传算法来训练人工神经网络和基于支持向量机的回归模型。从Nord Pool Spot和澳大利亚国家能源市场获得的真实数据证明了该方法的有效性。

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