Financial time series analysis and forecasting with Hilbert–Huang transform feature generation and machine learning. (3rd May 2021)

• Record Type: Journal Article
• Authors: Leung, Tim; Zhao, Theodore
• Is Part Of: Applied stochastic models in business and industry. Volume 37:Number 6(2021)
• Page Start: 993
• Page End: 1016
• Publication Date: 2021-05-03
• Subjects: empirical mode decomposition -- Hilbert transform -- machine learning -- time series; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2625
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract: 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.