Financial time series analysis and forecasting with Hilbert-Huang transform feature generation and machine learning

DOI10.1002/asmb.2625MaRDI QIDQ6579697

Theodore Zhao, Tim Leung

Publication date: 25 July 2024

Published in: Applied Stochastic Models in Business and Industry (Search for Journal in Brave)

zbMATH Keywords

time seriesHilbert transformmachine learningempirical mode decomposition

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Forecasting nonstationary time series based on Hilbert-Huang transform and machine learning
• An optimization based empirical mode decomposition scheme
• Data-driven time-frequency analysis
• Sparse mean-reverting portfolios via penalized likelihood optimization
• ADAPTIVE DATA ANALYSIS VIA SPARSE TIME-FREQUENCY REPRESENTATION
• Robust Locally Weighted Regression and Smoothing Scatterplots
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Applications of Hilbert–Huang transform to non‐stationary financial time series analysis
• The Elements of Statistical Learning

This page was built for publication: Financial time series analysis and forecasting with Hilbert-Huang transform feature generation and machine learning