Mixed random forest, cointegration, and forecasting gasoline prices

Escribano, Álvaro; Wang, Dandan

doi:10.1016/j.ijforecast.2020.12.008

Cited by 19 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As shown in Figure 13 , Artificial Neural Network (ANN) was a typical ML algorithm [ 31 , 32 , 33 , 34 , 35 , 36 ] used for predictive analysis of the f ov . Additionally, the ANN model was trained using the Adam optimizer algorithm [ 37 , 38 ].…”

Section: Ann-based Predictive Model For F Ovmentioning

confidence: 99%

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Xiao,

Li,

Lin

et al. 2024

Sensors

View full text Add to dashboard Cite

To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical–mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.

show abstract

Section: Ann-based Predictive Model For F Ovmentioning

confidence: 99%

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Xiao,

Li,

Lin

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to discover explanatory variables with nonlinear influences, threshold values, and closest parametric approximation, ref. [18] suggest a new mixed RF strategy for modeling. The methodology is implemented for weekly gasoline price estimates, which are cointegrated with global oil prices and exchange rates.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms

Sofianos,

Zaganidis,

Papadimitriou

et al. 2024

Energies

View full text Add to dashboard Cite

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers.

show abstract

ARIMAX Modelling of Ron97 Price with Crude Oil Price as an Exogenous Variable in Malaysian

Sarpong-Streetor

Sokkalingam

Othman

et al. 2021

Proceedings of the 6th International Conference on Fundamental and Applied Sciences

View full text Add to dashboard Cite

Mixed random forest, cointegration, and forecasting gasoline prices

Cited by 19 publications

References 28 publications

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN

Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms

ARIMAX Modelling of Ron97 Price with Crude Oil Price as an Exogenous Variable in Malaysian

Contact Info

Product

Resources

About