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. © 2012 Massimo Panella et al.
Forecasting energy commodity prices using neural networks / Panella, Massimo; Barcellona, Francesco; D'Ecclesia, RITA LAURA. - In: ADVANCES IN DECISION SCIENCES. - ISSN 2090-3359. - STAMPA. - 2012:Article ID 289810(2012), pp. 1-26. [10.1155/2012/289810]
Forecasting energy commodity prices using neural networks
PANELLA, Massimo;BARCELLONA, FRANCESCO;D'ECCLESIA, RITA LAURA
2012
Abstract
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. © 2012 Massimo Panella et al.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.