Neural network prediction of crude oil futures using B-splines

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

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
1 Downloads (Pure)


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.
Original languageEnglish
Article number105080
Pages (from-to)1-11
Number of pages11
JournalEnergy Economics
Early online date28 Dec 2020
Publication statusPublished - 1 Feb 2021


  • Crude oil futures
  • Functional data
  • Model confidence set
  • Neural network
  • Splines
  • Term structure


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