Probabilistic forecasting, linearity and nonlinearity hypoth…

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  • DOI 10.1080/0952813x.2019.1595167
  • OpenAlex ID W2938318244
  • Type Article
  • Language EN
  • Published 08 Apr 2019
  • Volume 33 (3)
  • Pages 383–404
  • Source Journal of Experimental & Theoretical Ar…
  • Publisher Taylor & Francis
  • FWCI 2.51
  • Countries 1
  • Institutions 1
  • Added 25 Apr 2019
  • Updated 12 Oct 2026
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Probabilistic forecasting, linearity and nonlinearity hypothesis tests with bootstrapped linear and nonlinear artificial neural network

Article 2019 EN

Time series can contain both linear and nonlinear components, and linear and nonlinear artificial neural networks (L&NL-ANNs) have been proposed to forecast them. Although L&NL-ANNs can produce well forecasting results, these neural networks have a handicap in point of view statistical science like other neural networks. The forecasts obtained from L&NL-ANNs may change depending on the time series…

19

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61

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5

Authors

2.51

FWCI

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5 authors
#AuthorInstitutionsORCID
1 Ufuk Yolcu Giresun University (TR)
2 Erol Eğrioğlu Corresponding Giresun University (TR)
3 Eren Baş Giresun University (TR)
4 Özge Cağcağ Yolcu Giresun University (TR)
5 Ali Zafer Dalar Giresun University (TR)

Data: OpenAlex