Forecasting energy commodity prices using neural networks
type: Item
instance of: scholarly article
title: Forecasting energy commodity prices using neural networks
zbMATH Open document ID: 1254.91683
DOI: 10.1155/2012/289810
author: Massimo Panella
author: Francesco Barcellona
author: Rita L. D'Ecclesia
published in: Advances in Decision Sciences
publication date: 2013-01-10T00:00:00Z
review text: Summary: A new machine learning approach for price modeling is proposed. The use of neural networks as an advanced signal processing tool may be successfully used to model and forecast energy commodity prices, such as crude oil, coal, natural gas, and electricity prices. Energy commodities have shown explosive growth in the last decade. They have become a new asset class used also for investment purposes. This creates a huge demand for better modeling as what occurred in the stock markets in the 1970s. Their price behavior presents unique features causing complex dynamics whose prediction is regarded as a challenging task. The use of a Mixture of Gaussian neural network may provide significant improvements with respect to other well-known models. We propose a computationally efficient learning of this neural network using the maximum likelihood estimation approach to calibrate the parameters. The optimal model is identified using a hierarchical constructive procedure that progressively increases the model complexity. Extensive computer simulations validate the proposed approach and provide an accurate description of commodities prices dynamics.
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6123898
zbMATH Keywords: machine learning approach
zbMATH Keywords: price modeling
describes a project that uses: PRMLT
describes a project that uses: sapa
MaRDI profile type: Publication
Wikidata QID: Q58697772
full work available at URL: https://doi.org/10.1155/2012/289810
OpenAlex ID: W1978982237
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description: scientific article; zbMATH DE number 6123898