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• W3118147777 startingPage "105080" @default.
• W3118147777 abstract "We propose two ways to improve the forecasting accuracy of a focused time-delay neural network (FTDNN) that forecasts the term structure of crude oil futures. Our results show that a convergence based FTDNN makes consistently more accurate predictions than the fixed-epoch FTDNN in Barunik and Malinska (2016). Further, we suggest using basis splines (B-splines), instead of Nelson-Siegel functions, to fit the term structure curves. The empirical results show that the B-spline expansions lead to consistently better 1 and 3 months ahead predictions compared to the convergence based FTDNN. We also explore conditions under which the B-spline based approach may be better for longer-term predictions." @default.
• W3118147777 created "2021-01-05" @default.
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• W3118147777 date "2021-02-01" @default.
• W3118147777 modified "2023-10-10" @default.
• W3118147777 title "Neural network prediction of crude oil futures using B-splines" @default.
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• W3118147777 doi "https://doi.org/10.1016/j.eneco.2020.105080" @default.
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