Effective crude oil price forecasting using new text-based and big-data-driven model

Measurement - Tập 168 - Trang 108468 - 2021
Binrong Wu1, Lin Wang1, Sheng-Xiang Lv2, Yu-Rong Zeng3
1School of Management, Huazhong University of Science and Technology, Wuhan 430074, China
2School of Business Administration, Guangdong University of Finance and Economics, Guangzhou, 510320, China
3School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China

Tài liệu tham khảo

Abdollahi, 2020, A novel hybrid model for forecasting crude oil price based on time series decomposition, Appl. Energy, 267, 10.1016/j.apenergy.2020.115035 Yu, 2016, A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting, Eng. Appl. Artif. Intell., 47, 110, 10.1016/j.engappai.2015.04.016 Bildirici, 2020, Analyzing crude oil prices under the impact of COVID-19 by using LSTARGARCHLSTM, Energies, 13, 2980, 10.3390/en13112980 Yu, 2019, Online big data-driven oil consumption forecasting with Google Trends, Int. J. Forecast., 35, 213, 10.1016/j.ijforecast.2017.11.005 Li, 2015, How does Google search affect trader positions and crude oil price?, Econ. Model., 49, 162, 10.1016/j.econmod.2015.04.005 Fronzetti Colladon, 2020, Forecasting election results by studying brand importance in online news, Int. J. Forecast., 36, 414, 10.1016/j.ijforecast.2019.05.013 Atalla, 2016, Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB surveys, Int. J. Forecast., 32, 1178, 10.1016/j.ijforecast.2015.09.009 Zhao, 2018, A novel method based on numerical fitting for oil price trend forecasting, Appl. Energy, 220, 154, 10.1016/j.apenergy.2018.03.060 Bekiroglu, 2018, Predictive analytics of crude oil price by utilizing the intelligent model search engine, Appl. Energy, 228, 2387, 10.1016/j.apenergy.2018.07.071 Liu, 2019, Empirical mode decomposition based hybrid ensemble model for electrical energy consumption forecasting of the cement grinding process, Measurement, 138, 314, 10.1016/j.measurement.2019.02.062 Shahbaz, 2015, Analyzing time–frequency relationship between oil price and exchange rate in Pakistan through wavelets, J. Appl. Statistics, 42, 690, 10.1080/02664763.2014.980784 Ma, 2017, A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting, Appl. Soft Comput., 54, 296, 10.1016/j.asoc.2017.01.033 Wang, 2018, A semi-heterogeneous approach to combining crude oil price forecasts, Inf. Sci., 460, 279, 10.1016/j.ins.2018.05.026 Elshendy, Mohammed, & Colladon et al., 2018. Using four different online media sources to forecast the crude oil price. Journal of Information Science, 44(3), 408-421. Wex, F., Widder, N., Liebmann, M., et al., 2013. Early warning of impending oil crises using the predictive power of online news stories. In: 2013 46th Hawaii International Conference on System Sciences, pp. 1512-1521. Li, 2019, Text-based crude oil price forecasting: A deep learning approach, Int. J. Forecast., 35, 1548, 10.1016/j.ijforecast.2018.07.006 Vecchio, 2018, Creating value from social big data: implications for smart tourism destinations, Inf. Process. Manage., 54, 847, 10.1016/j.ipm.2017.10.006 Kim, 2019, Can media forecast technological progress?: A text-mining approach to the on-line newspaper and blog's representation of prospective industrial technologies, Inf. Process. Manage., 56, 1506, 10.1016/j.ipm.2018.10.017 Hemmatian, 2019, A survey on classification techniques for opinion mining and sentiment analysis, Artif. Intell. Rev., 52, 1495, 10.1007/s10462-017-9599-6 Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Agarwal, 2017, A deep network model for paraphrase detection in short text messages, Inf. Process. Manage., 54, 922, 10.1016/j.ipm.2018.06.005 Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Sig. Process., 115, 213, 10.1016/j.ymssp.2018.05.050 Nassirtoussi, 2015, Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment, Expert Syst. Appl., 42, 306, 10.1016/j.eswa.2014.08.004 Ye, 2006, Forecasting short-run crude oil price using high-and low-inventory variables, Energy Policy, 34, 2736, 10.1016/j.enpol.2005.03.017 Dragomiretskiy, 2013, Variational mode decomposition, IEEE Trans. Signal Process., 62, 531, 10.1109/TSP.2013.2288675 Groth, 2011, An intraday market risk management approach based on textual analysis, Decis. Support Syst., 50, 680, 10.1016/j.dss.2010.08.019 Cho, 2018, Using social network analysis and gradient boosting to develop a soccer win–lose prediction model, Eng. Appl. Artif. Intell., 72, 228, 10.1016/j.engappai.2018.04.010 Makarenkov, 2019, Implicit dimension identification in user-generated text with LSTM networks, Inf. Process. Manage., 55, 1880, 10.1016/j.ipm.2019.02.007 Liu, 2017, Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network, Energies, 10, 1453, 10.3390/en10101453 Abdel-Nasser, 2019, Accurate photovoltaic power forecasting models using deep LSTM-RNN, Neural Comput. Appl., 31, 2727, 10.1007/s00521-017-3225-z Cai, 2019, Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques, Appl. Energy, 236, 1078, 10.1016/j.apenergy.2018.12.042 Zhang, 2016, Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods, Energy Convers. Manage., 112, 208, 10.1016/j.enconman.2016.01.023 Lucheroni, 2019, Scenario generation and probabilistic forecasting analysis of spatio-temporal wind speed series with multivariate autoregressive volatility models, Appl. Energy, 239, 1226, 10.1016/j.apenergy.2019.02.015 Lv, 2018, Stacked autoencoder with echo-state regression for tourism demand forecasting using search query data, Appl. Soft Comput., 73, 119, 10.1016/j.asoc.2018.08.024 Ju, 2013, Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting, Appl. Math. Model., 37, 9643, 10.1016/j.apm.2013.05.016 Peng, 2020, Effective long short-term memory with fruit fly optimization algorithm for time series forecasting, Soft Comput., 24, 15059, 10.1007/s00500-020-04855-2 Wang, 2015, Back propagation neural network with adaptive differential evolution algorithm for time series forecasting, Expert Syst. Appl., 42, 855, 10.1016/j.eswa.2014.08.018 Hu, 2020, Effective energy consumption forecasting using enhanced bagged echo state network, Energy, 193, 10.1016/j.energy.2019.116778 Hu, 2020, Forecasting energy consumption and wind power generation using deep echo state network, Renewable Energy, 154, 598, 10.1016/j.renene.2020.03.042