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.
In this paper we use a kernel-based approach to Crude Oil price prediction which should allow us to set up efficient risk management strategies. Practitioners find strong evidence that investor flows follow prices so Commodity investments are likely to continue to grow, and we believe this will drive an increasing importance for methodologies like Neural Networks for risk quantification, measurement and management. Crude Oil prices for both Brent and WTI in the last 12 year period are used to provide an accurate analysis for both time series. Four different Neural Network models are used. The superior model is the neurofuzzy network based on Sugeno first-order type rules, also known as the Adaptive Neuro-Fuzzy Inference System method, which provides both an accurate prediction of prices and their probability distribution
The dynamics of commodity prices has become a major field of analysis in the last 20 years. Standard econometric procedures to describe the behavior of prices have not been able to provide accurate description of the real dynamics. In this paper we apply filter banks to predict prices of specific energy commodities: crude oil, natural gas and electricity, which play a crucial role in the international economic and financial context. Given the high volatility of energy commodity prices, an accurate short term prediction allows to set adequate risk management strategies for producers, retailers and consumers. Filter banks for subband decompositions of the sequences to be predicted are proposed in the paper, allowing the implementation of a parallel computing system to get faster and more accurate implementation. The prediction system is based on a neural model trained on each subband according to specific training and prediction techniques
In the last decade the increasing volatility of petroleum markets has challenged time series analysts to produce highly predictive models. Crude Oil is a major driver of the global economy and its price fluctuations are a key indicator for producers, consumers and investors. With investors following the longerterm upward trend in Energy prices Commodity investments, we believe this will drive an increasing importance for methodologies like neurofuzzy networks for risk quantification, measurement and management. The data used is Crude Oil prices for both Brent and WTI in the 10 year period from 2001 to 2010. We will prove that the neurofuzzy approach based on ANFIS networks compare favorably with respect to other standard and neural models and it is able to achieve useful performances in terms of accurate prediction of prices and their probability distribution.
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