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Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods
7 references·2 citing·31 second hop
Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods (2019)Data Driven Natural Gas Spot Price Predic…Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm (2019)Data-Driven Natural Gas Spot …Short-term forecasting of natural gas prices using machine learning and feature selection algorithms (2017)Short-term forecasting of nat…Predicting the direction of stock market prices using random forest (2016)Predicting the direction of s…A new metric of absolute percentage error for intermittent demand forecasts (2016)A new metric of absolute perc…Another look at measures of forecast accuracy (2006)Another look at measures of f…A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)A Decision-Theoretic Generali…ON THE VALIDATION OF MODELS (1981)ON THE VALIDATION OF MODELSNatural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model (2023)Natural gas spot price predic…Daily natural gas price forecasting by a weighted hybrid data-driven model (2020)Daily natural gas price forec…Forecasting methods and applications (1993)Automatic Time Series Forecasting: The forecast Package for R (2008)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms (2021)Forecast evaluation for data scientists: common pitfalls and best practices (2022)Multikonferenz Wirtschaftsinformatik 2010 (2010)Energy price prediction using data-driven models: A decade review (2020)Forecasting energy prices using a novel hybrid model with variational mode decomposition (2022)The Capital Asset Pricing Model: Theory and Evidence (2004)Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks (2021)Principles of forecasting : a handbook for researchers and practitioners (2001)Principles of Forecasting. A Handbook for Researchers and Practitioners (2002)Modeling and forecasting petroleum futures volatility (2006)A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction (2021)A residual driven ensemble machine learning approach for forecasting natural gas prices: analyses for pre-and during-COVID-19 phases (2022)Back to Basics: The Power of the Multilayer Perceptron in Financial Time Series Forecasting (2024)Impact of Russian-Ukraine war on clean energy, conventional energy, and metal markets: Evidence from event study approach (2022)Role of hydroelectricity and natural gas consumption on environmental sustainability in the United States: Evidence from novel time-frequency approaches (2022)Asymmetric effects of geopolitical risks and uncertainties on green bond markets (2023)Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps (2025)The Nonstochastic Multiarmed Bandit Problem (2002)Prediction, Learning, and Games (2006)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)On the use of dimensioned measures of error to evaluate the performance of spatial interpolators (2006)Components of information for multiple resolution comparison between maps that share a real variable (2007)Revisiting the concept of a symmetric index of agreement for continuous datasets (2016)Deep Robust Reinforcement Learning for Practical Algorithmic Trading (2019)ST-Trader: A Spatial-Temporal Deep Neural Network for Modeling Stock Market Movement (2021)A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems (2024)
this work references cited by second hop

References

  • 1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting20,469 cited
  • 2006Another look at measures of forecast accuracy5,736 cited
  • 1981ON THE VALIDATION OF MODELS4,904 cited
  • 2016A new metric of absolute percentage error for intermittent demand forecasts1,150 cited
  • 2016Predicting the direction of stock market prices using random forest222 cited
  • 2017Short-term forecasting of natural gas prices using machine learning and feature selection algorithms98 cited
  • 2019Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm84 cited

Cited by

  • 2020Daily natural gas price forecasting by a weighted hybrid data-driven model53 cited
  • 2023Natural gas spot price prediction research under the background of Russia-Ukraine conflict - based on FS-GA-SVR hybrid model41 cited

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