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Neural network prediction of crude oil futures using B-splines

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  • Butler, Sunil
  • Kokoszka, Piotr
  • Miao, Hong
  • Shang, Han Lin

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.

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  • Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    • Handle: RePEc:eee:eneeco:v:94:y:2021:i:c:s0140988320304205
    DOI: 10.1016/j.eneco.2020.105080
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