  We present the method of complementary ensemble empirical mode decomposition
(CEEMD) 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 (SVM), and long
short-term memory (LSTM) neural network. Using empirical financial data, we
compare several HHT-enhanced machine learning models in terms of forecasting
performance.
