Financial Time Series Analysis and Forecasting with HHT Feature Generation and Machine Learning

TL;DR

This paper introduces a novel approach combining CEEMD and HHT to analyze nonstationary financial data, generating features for machine learning models to improve forecasting accuracy.

Contribution

It presents a new method of feature extraction using HHT for financial time series, enhancing machine learning forecasting models.

Findings

HHT features improve forecasting accuracy

Comparison shows HHT-enhanced models outperform traditional methods

Effective analysis of nonstationary financial data

Abstract

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.