Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model

A. Usha Ruby (), J. George Chellin Chandran (), B. N. Chaithanya (), T. J. Swasthika Jain () and Renuka Patil ()
Additional contact information
A. Usha Ruby: VIT Bhopal University
J. George Chellin Chandran: VIT Bhopal University
B. N. Chaithanya: GITAM School of Technology
T. J. Swasthika Jain: GITAM School of Technology
Renuka Patil: GITAM School of Technology

Computational Economics, 2024, vol. 64, issue 2, No 22, 1295-1314

Abstract: Abstract There is an urgent need for the prediction of oil price; in addition, for various small and large industries, individuals, and the government, it is a blessing. Nevertheless, owing to the nonlinear inherent in crude oil price (COP), market inter-relationship in oil price time series, chaotic behavior of the COP, and inherent fractality in oil price, the prediction of COP is tedious. By employing a polyharmony spline centered recurrent neural network (PS-RNN) the work presented a new framework of COP prediction to tackle those problems. By deploying the cubic hermite spline grounded on empirical mode decomposition, which offers decomposed data, the system tackles the intrinsic characteristics of fluctuating data. The knowledgeable features are extracted using simple moving average (SMA), and exponential moving average technical indicators. By utilizing the Gaussian distribution-centric Aquila optimization that aids to endure nonlinear inherent of data within low computation time, the most related features are elected. Finally, the chosen features get trained to PS-RNN. Regarding mean absolute error, root mean squared error , mean absolute percent error (MAPE), along with Symmetric MAPE, the system obtains a low relative fault along with prevents misprediction of data; in addition, sustains higher to prevailing techniques.

Keywords: Crude oil; Cubic hermite spline based on empirical mode decomposition (CHS-EMD); Gaussian distribution-based Aquila optimization (GD-AO); Polyharmonic spline based recurrent neural network (PS-RNN); Simple moving average (SMA); Exponential moving average (EMA) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-023-10460-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:64:y:2024:i:2:d:10.1007_s10614-023-10460-w

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-023-10460-w

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().