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Forecasting crude oil price with a new hybrid approach and multi-source data
16 references·11 citing·50 second hopsampled
Forecasting crude oil price with a new hybrid approach and multi-source data (2021)Forecasting crude oil price with a new hy…Machine learning in energy economics and finance: A review (2019)Machine learning in energy ec…Hybrid structures in time series modeling and forecasting: A review (2019)Hybrid structures in time ser…Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors? (2019)Forecasting crude oil prices …Text-based crude oil price forecasting: A deep learning approach (2018)Text-based crude oil price fo…Forecasting tourist arrivals with machine learning and internet search index (2018)Forecasting tourist arrivals …A hybrid ETS–ANN model for time series forecasting (2017)A hybrid ETS–ANN model for ti…Forecasting daily stock market return using dimensionality reduction (2016)Forecasting daily stock marke…A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting (2015)A novel decomposition ensembl…A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015)A decomposition–ensemble mode…Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach (2014)Can Google data improve the f…Investor Flows and the 2008 Boom/Bust in Oil Prices (2013)Investor Flows and the 2008 B…In Search of Attention (2011)In Search of AttentionInternational evidence on crude oil price dynamics: Applications of ARIMA-GARCH models (2010)International evidence on cru…Forecasting crude oil market volatility: Further evidence using GARCH-class models (2010)Forecasting crude oil market …Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Forecasting crude oil price w…Market 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 (1998)Market efficiency, long-term …Intelligent crude oil price probability forecasting: Deep learning models and industry applications (2024)Intelligent crude oil price p…A blending ensemble learning model for crude oil price forecasting (2024)A blending ensemble learning …Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model (2023)Crude oil price forecasting w…Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach (2023)Can multi-source heterogeneou…A dynamic clustering ensemble learning approach for crude oil price forecasting (2023)A dynamic clustering ensemble…Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning (2022)Interval prediction approach …Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images (2022)Forecasting turning points in…EcoForecast: An interpretable data-driven approach for short-term macroeconomic forecasting using N-BEATS neural network (2022)EcoForecast: An interpretable…Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm (2022)Deterministic and uncertainty…A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting (2022)A new secondary decomposition…Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework (2021)Multi-Perspective Crude Oil P…Time Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Time series prediction: Forecasting the future and understanding the past (1994)Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold (2013)A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series (2020)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)A novel hybrid method for crude oil price forecasting (2015)Forecasting oil price realized volatility using information channels from other asset classes (2017)Geopolitical risk and oil volatility: A new insight (2019)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)The Cross-Section of Expected Stock Returns (1992)Common risk factors in the returns on stocks and bonds (1993)Investor Psychology and Asset Pricing (2001)More Than Words: Quantifying Language to Measure Firms' Fundamentals (2008)Presidential Address: Discount Rates (2011)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)New developments in tourism and hotel demand modeling and forecasting (2017)Forecasting sales in the supply chain: Consumer analytics in the big data era (2018)Forecasting tourism demand with multisource big data (2020)Fuzzy sets (1965)Fuzzy sets (1996)Time series forecasting using a hybrid ARIMA and neural network model (2003)A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction (2017)ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module (2018)Stock market prediction using machine learning classifiers and social media, news (2020)Numerical distribution functions of likelihood ratio tests for cointegration (1999)Predicting the Present with Google Trends (2012)Daily tourism volume forecasting for tourist attractions (2020)Review of tourism forecasting research with internet data (2020)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions (2020)Does Good ESG Lead to Better Financial Performances by Firms? 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References

  • 2011In Search of Attention3,088 cited
  • 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.12,572 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2013Investor Flows and the 2008 Boom/Bust in Oil Prices616 cited
  • 2019Machine learning in energy economics and finance: A review438 cited
  • 2016Forecasting daily stock market return using dimensionality reduction395 cited
  • 2018Forecasting tourist arrivals with machine learning and internet search index381 cited
  • 2010Forecasting crude oil market volatility: Further evidence using GARCH-class models374 cited
  • 2010International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models346 cited
  • 2014Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach331 cited
  • 2018Text-based crude oil price forecasting: A deep learning approach305 cited
  • 2019Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?290 cited
  • 2019Hybrid structures in time series modeling and forecasting: A review240 cited

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  • 2017A hybrid ETS–ANN model for time series forecasting217 cited
  • 2015A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting204 cited
  • 2015A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting204 cited
  • Cited by

    • 2022Forecasting turning points in stock price by applying a novel hybrid CNN-LSTM-ResNet model fed by 2D segmented images61 cited
    • 2023Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach58 cited
    • 2022Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm57 cited
    • 2022A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting54 cited
    • 2024A blending ensemble learning model for crude oil price forecasting48 cited
    • 2022EcoForecast: An interpretable data-driven approach for short-term macroeconomic forecasting using N-BEATS neural network47 cited
    • 2021Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework46 cited
    • 2023Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model45 cited
    • 2022Interval prediction approach to crude oil price based on three-way clustering and decomposition ensemble learning40 cited
    • 2023A dynamic clustering ensemble learning approach for crude oil price forecasting40 cited
    • 2024Intelligent crude oil price probability forecasting: Deep learning models and industry applications37 cited