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https://doi.org/10.2139/ssrn.3992383

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.apenergy.2019.114033openalex-extracted
A multi-scale method for forecasting oil price with multi-factor search engine data
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A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting
inferredcites →https://doi.org/10.1016/j.enconman.2019.05.005openalex-extracted
A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting
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Modeling and forecasting cointegrated relationships among heavy oil and product prices
inferredcites →https://doi.org/10.1016/j.eneco.2008.05.003openalex-extracted
Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm
inferredcites →https://doi.org/10.1016/j.eneco.2010.04.009openalex-extracted
International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models
inferredcites →https://doi.org/10.1016/j.eneco.2010.07.009openalex-extracted
Forecasting crude oil market volatility: Further evidence using GARCH-class models
inferredcites →https://doi.org/10.1016/j.eneco.2011.07.018openalex-extracted
Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling
inferredcites →https://doi.org/10.1016/j.eneco.2011.08.004openalex-extracted
A nonparametric GARCH model of crude oil price return volatility
inferredcites →https://doi.org/10.1016/j.eneco.2015.09.003openalex-extracted
Forecasting the real prices of crude oil under economic and statistical constraints
inferredcites →https://doi.org/10.1016/j.eneco.2016.09.020openalex-extracted
Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?
inferredcites →https://doi.org/10.1016/j.eneco.2017.05.023openalex-extracted
A deep learning ensemble approach for crude oil price forecasting
inferredcites →https://doi.org/10.1016/j.eneco.2017.07.014openalex-extracted
Google search keywords that best predict energy price volatility
inferredcites →https://doi.org/10.1016/j.eneco.2017.09.010openalex-extracted
Influential factors in crude oil price forecasting
inferredcites →https://doi.org/10.1016/j.eneco.2018.02.004openalex-extracted
Forecasting the WTI crude oil price by a hybrid-refined method
inferredcites →https://doi.org/10.1016/j.eneco.2019.07.009openalex-extracted
Monthly crude oil spot price forecasting using variational mode decomposition
inferredcites →https://doi.org/10.1016/j.energy.2018.01.007openalex-extracted
Oil price forecasting using a hybrid model
inferredcites →https://doi.org/10.1016/j.energy.2018.05.146openalex-extracted
A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting
inferredcites →https://doi.org/10.1016/j.engappai.2015.04.016openalex-extracted
A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting
inferredcites →https://doi.org/10.1016/j.engappai.2021.104217openalex-extracted
Forecasting crude oil price with a new hybrid approach and multi-source data
inferredcites →https://doi.org/10.1016/j.ijforecast.2015.02.006openalex-extracted
Forecasting crude oil market volatility: A Markov switching multifractal volatility approach
inferredcites →https://doi.org/10.1016/j.ijforecast.2018.03.009openalex-extracted
Crude oil price forecasting based on internet concern using an extreme learning machine
inferredcites →https://doi.org/10.1016/j.ijforecast.2018.07.006openalex-extracted
Text-based crude oil price forecasting: A deep learning approach
inferredcites →https://doi.org/10.1016/j.ijforecast.2020.02.007openalex-extracted
Forecasting global equity market volatilities
inferredcites →https://doi.org/10.1016/j.jeconbus.2012.03.002openalex-extracted
A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices
inferredcites →https://doi.org/10.1016/j.knosys.2019.105006openalex-extracted
Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network
inferredcites →https://doi.org/10.1016/j.measurement.2020.108468openalex-extracted
Effective crude oil price forecasting using new text-based and big-data-driven model
inferredcites →https://doi.org/10.1016/j.medengphy.2008.04.005openalex-extracted
Measuring complexity using FuzzyEn, ApEn, and SampEn
inferredcites →https://doi.org/10.1016/j.patrec.2010.03.014openalex-extracted
Variable selection using random forests
inferredcites →https://doi.org/10.1016/j.petrol.2020.107013openalex-extracted
Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network
inferredcites →https://doi.org/10.1016/j.physa.2017.04.160openalex-extracted
Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis
inferredcites →https://doi.org/10.1016/j.physa.2020.124178openalex-extracted
Forecasting crude oil price with multilingual search engine data
inferredcites →https://doi.org/10.1016/j.physa.2021.126045openalex-extracted
A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy
inferredcites →https://doi.org/10.1016/j.resourpol.2018.11.006openalex-extracted
Point and density forecasts of oil returns: The role of geopolitical risks
inferredcites →https://doi.org/10.1016/j.scs.2019.101657openalex-extracted
Daily urban air quality index forecasting based on variational mode decomposition, sample entropy and LSTM neural network
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-extracted
Random Forests
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-extracted
Gene Selection for Cancer Classification using Support Vector Machines
inferredcites →https://doi.org/10.1080/07350015.1992.10509922openalex-extracted
A Simple Nonparametric Test of Predictive Performance
inferredcites →https://doi.org/10.1080/07350015.2011.648859openalex-extracted
Real-Time Forecasts of the Real Price of Oil
inferredcites →https://doi.org/10.1109/tnsre.2007.897025openalex-extracted
Characterization of Surface EMG Signal Based on Fuzzy Entropy
inferredcites →https://doi.org/10.1109/tsp.2013.2288675openalex-extracted
Variational Mode Decomposition
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-extracted
Long Short-Term Memory
inferredcites →https://doi.org/10.1198/073500102753410444openalex-extracted
Comparing Predictive Accuracy
inferredcites →https://doi.org/10.12973/ejmste/77926openalex-extracted
Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting
inferredcites →https://doi.org/10.3390/en11071882openalex-extracted
Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning
inferredcites →https://doi.org/10.3390/en13071852openalex-extracted
Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market
inferredcites →https://doi.org/10.3390/genes9060301openalex-extracted
Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4186710openalex-extracted
A Novel Multivariate Decomposition Ensemble Model with News Text for Crude Oil Price Forecasting
inferred← citeshttps://doi.org/10.2139/ssrn.4783554openalex-extracted
Intelligent Crude Oil Price Probability Forecasting: Deep Learning Models and Industry Applications
inferred← citeshttps://doi.org/10.2139/ssrn.5393608openalex-snapshot
Forecasting Short-Term Wti Crude Oil Prices: Are State-of-The-Art Deep Learning Models More Accurate than Traditional Econometric Models?
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