Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
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- Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023. "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, vol. 39(2), pages 884-900.
References listed on IDEAS
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More about this item
Keywords
Deep learning; NBEATS and NBEATSx models; Interpretable neural network; Time series decomposition; Fourier series; Electricity price forecasting;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
- Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-04-26 (Big Data)
- NEP-CMP-2021-04-26 (Computational Economics)
- NEP-ENE-2021-04-26 (Energy Economics)
- NEP-FOR-2021-04-26 (Forecasting)
- NEP-ORE-2021-04-26 (Operations Research)
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