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ArticleForecasting crude oil price with a new hybrid approach and multi-source data

Yifan Yang2021

Article2021

Forecasting crude oil price with a new hybrid approach and multi-source data

Yifan Yang · Ju’e Guo · Shaolong Sun · Yixin Li

73 citations·0/0 files stored·Engineering Applications of Artificial Intelligence

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Stock Market Forecasting Methods Forecasting Techniques and Applications Market Dynamics and Volatility

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References

2011In Search of Attention 1998Market efficiency, long-term returns, and behavioral finance1The comments of Brad Barber, David Hirshleifer, S.P. Kothari, Owen Lamont, Mark Mitchell, Hersh Shefrin, Robert Shiller, Rex Sinquefield, Richard Thaler, Theo Vermaelen, Robert Vishny, Ivo Welch, and a referee have been helpful. Kenneth French and Jay Ritter get special thanks.1 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2013Investor Flows and the 2008 Boom/Bust in Oil Prices 2019Machine learning in energy economics and finance: A review 2016Forecasting daily stock market return using dimensionality reduction 2018Forecasting tourist arrivals with machine learning and internet search index 2010Forecasting crude oil market volatility: Further evidence using GARCH-class models

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Year2021

Typearticle

Published2021-03-10

VenueEngineering Applications of Artificial Intelligence

ISSN-L0952-1976

Languageen

Citations73

Files0 public / 0

ShelfStock Market Forecasting Methods

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doi10.1016/j.engappai.2021.104217

openalex_workW3135312874

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2022

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2024A blending ensemble learning model for crude oil price forecasting

2022EcoForecast: An interpretable data-driven approach for short-term macroeconomic forecasting using N-BEATS neural network

2021Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework

2023Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model