Oil price volatility prediction using out-of-sample analysis – Prediction efficiency of individual models, combination methods, and machine learning based shrinkage methods

Cheng, WeiJin; Ming, Kai; Ullah, Mirzat

doi:10.1016/j.energy.2024.131496

Energy

2024

DOI: 10.1016/j.energy.2024.131496

|View full text |Cite

Oil price volatility prediction using out-of-sample analysis – Prediction efficiency of individual models, combination methods, and machine learning based shrinkage methods

WeiJin Cheng,

Kai Ming,

Mirzat Ullah

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

Dutta,

Bouri

2024

Energy Strategy Reviews

View full text Add to dashboard Cite

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

Dutta,

Bouri

2024

Energy Strategy Reviews

View full text Add to dashboard Cite

Uncertainty Quantification Method for Trend Prediction of Oil Well Time Series Data Based on SDMI Loss Function

Shen,

Wang,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

IoT sensors in oilfields gather real-time data sequences from oil wells. Accurate trend predictions of these data are crucial for production optimization and failure forecasting. However, oil well time series data exhibit strong nonlinearity, requiring not only precise trend prediction but also the estimation of uncertainty intervals. This paper first proposed a data denoising method based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to reduce the noise present in oil well time series data. Subsequently, an SDMI loss function was introduced, combining the respective advantages of Soft Dynamic Time Warping and Mean Squared Error (MSE). The loss function additionally accepts the upper and lower bounds of the uncertainty prediction interval as input and is optimized with the prediction sequence. By predicting the data of the next 48 data points, the prediction results using the SDMI loss function and the existing three common loss functions are compared on multiple data sets. The prediction results before and after data denoising are compared and the results of predicting the uncertainty interval are shown. The experimental results demonstrate that the average coverage rate of the predicted uncertainty intervals across data from seven wells is 81.4%, and the prediction results accurately reflect the trends in real data.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Oil price volatility prediction using out-of-sample analysis – Prediction efficiency of individual models, combination methods, and machine learning based shrinkage methods

Cited by 2 publications

References 67 publications

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

Uncertainty Quantification Method for Trend Prediction of Oil Well Time Series Data Based on SDMI Loss Function

Contact Info

Product

Resources

About