Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?

Do Machine Learning Techniques and Dynamic Methods Help Forecast US Natural Gas Crises?

Our study combines machine learning techniques and dynamic moving window and expanding window methods to predict crises in the US natural gas market. Specifically, as machine learning models, we employ extreme gradient boosting (XGboost), support vector machines (SVMs), a logistic regression (LogR),...

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Bibliographic Details
Main Authors: Wenting Zhang, Shigeyuki Hamori
Format: Article
Language:English
Published: MDPI AG 2020-05-01
Series:Energies
Subjects:
dynamic approaches
forecasting
logistic regression
random forests
support vector machines
US natural gas crises
Online Access:https://www.mdpi.com/1996-1073/13/9/2371

Internet

https://www.mdpi.com/1996-1073/13/9/2371

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