We present the method of complementary ensemble empirical mode decomposition and Hilbert–Huang transform (HHT) for analyzing nonstationary financial time series. This noise-assisted approach decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the associated instantaneous energy-frequency spectrum to illustrate the properties of various timescales embedded in the original time series. Using HHT, we generate a collection of new features and integrate them into machine learning models, such as regression tree ensemble, support vector machine, and long short-term memory neural network. Using empirical financial data, we compare several HHT-enhanced machine learning models in terms of forecasting performance.

中文翻译：

---

使用 Hilbert-Huang 变换特征生成和机器学习进行金融时间序列分析和预测

我们提出了用于分析非平稳金融时间序列的互补集合经验模态分解和希尔伯特-黄变换 (HHT) 方法。这种噪声辅助方法将任何时间序列分解为许多固有模式函数，以及相应的瞬时幅度和瞬时频率。模式的不同组合允许我们使用不同时间尺度的分量来重建时间序列。然后，我们应用希尔伯特谱分析来定义和计算相关的瞬时能量频谱，以说明嵌入在原始时间序列中的各种时间尺度的属性。使用 HHT，我们生成一组新特征并将它们集成到机器学习模型中，例如回归树集成、支持向量机、和长短期记忆神经网络。使用经验财务数据，我们在预测性能方面比较了几个 HHT 增强的机器学习模型。