Optimized Multivariate Adaptive Regression Splines for Predicting Crude Oil Demand in Saudi Arabia
This paper presents optimized linear regression with multivariate adaptive regression splines (LR-MARS) for predicting crude oil demand in Saudi Arabia based on social spider optimization (SSO) algorithm. The SSO algorithm is applied to optimize LR-MARS performance by fine-tuning its hyperparameters...
Saved in:
| Main Authors: | Eman H. Alkhammash, Abdelmonaim Fakhry Kamel, Saud M. Al-Fattah, Ahmed M. Elshewey |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/8412895 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
ANALISIS KINERJA KARYAWAN MENGGUNAKAN MULTIVARIATE ADAPTIVE REGRESSION SPLINES
by: ARNEZDA PUTRI, et al.
Published: (2020-06-01) -
Impact of crude oil price uncertainty on systematic risk in the Saudi Arabia banking sector
by: Mohamed Amin Chakroun, et al.
Published: (2025-12-01) -
Nonparametric regression estimation using multivariable truncated splines for binary response data
by: Afiqah Saffa Suriaslan, et al.
Published: (2025-06-01) -
EVALUATION OF MULTIVARIATE ADAPTIVE REGRESSION SPLINES ON IMBALANCED DATASET FOR POVERTY CLASSIFICATION IN BENGKULU PROVINCE
by: Idhia Sriliana, et al.
Published: (2025-04-01) -
MODELING HYPERTENSION DISEASE RISK IN INDONESIA USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND BINARY LOGISTIC REGRESSION APPROACHES
by: Nur Chamidah, et al.
Published: (2024-10-01)