A novel hybrid method for crude oil price forecasting

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The VEC-NAR model for short-term forecasting of oil prices (2019, methodology): “The short-term forecast of oil price has three main characteristics, namely, lag, nonlinearity, and interrelationship amongdifferent oilmarkets (Yu et al., 2008; Bhar et al., 2008; Song and Li, 2015; Zhang et al., 2015; Chen et al., 2016).”
A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “Previous studies assume data frequency to be an important factor that a ﬀ ects the accuracy of crude oil price forecasting [ 1 – 31 ].”
Volatility forecasting of crude oil market: A new hybrid method (2018, background): “…three categories: (i) time series volatility models; (ii) implied volatility models; and (iii) hybrid models. wileyonlinelibrary.com/journa J. L. Zhang, Zhang, and Zhang (2015) presented a relatively systematic review of existing literature concerning typical models used to forecast crude oil…”
Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? (2019, background): “…chain to have different GARCH behaviors, i.e., different volatility structures, so as to extend the GARCH model to the dynamic forms and realize better estimating and forecasting performance (Haas, Mittnik,& Paolella, 2004; Klaassen, 2002; Marcucci, 2005; Zhang&Wang, 2015; Zhang & Zhang, 2015).”