SCIENCE@home

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

Wenting Zhang,Shigeyuki Hamori

Published 2020 in Energies

ABSTRACT

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), random forests (RFs), and neural networks (NNs). The data set used to develop the model covers the period 1994 to 2019 and contains 121 explanatory variables, including those related to crude oil, stock markets, US bond and gold futures, the CBOE Volatility Index (VIX) index, and agriculture futures. To the best of our knowledge, this study is the first to combine machine learning techniques with dynamic approaches to predict US natural gas crises. To improve the model’s prediction accuracy, we applied a suite of parameter-tuning methods (e.g., grid-search) to select the best-performing hyperparameters for each model. Our empirical results demonstrated very good prediction accuracy for US natural gas crises when combining the XGboost model with the dynamic moving window method. We believe our findings will be useful to investors wanting to diversify their portfolios, as well as to policymakers wanting to take preemptive action to reduce losses.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Energies

  • Publication date

    2020-05-09

  • Fields of study

    Business, Computer Science, Economics, Environmental Science

  • Identifiers
    DOI 10.3390/en13092371
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-40 of 40 references · Page 1 of 1

CITED BY

Showing 1-7 of 7 citing papers · Page 1 of 1