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A Computational Method for Predicting the Entropy of Energy Market Time Series

  • Francesco Benedetto
  • Gaetano Giunta
  • Loretta Mastroeni
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 682)

Abstract

This work introduces a new computational method for evaluating the predictability of energy market time series, by predicting the entropy of the series. According to conventional entropy-based analysis, high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict the entropy regarding the future behavior of a series, based on the observation of historical data. Our prediction is performed according to the optimum least squares minimization algorithm, as happens in conventional computational minimization approaches. Preliminary results, applied to energy commodities, show the efficacy of the proposed method for application to energy market time series.

Keywords

Maximum Entropy Approximate Entropy Entropy Analysis Autocovariance Function Energy Commodity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francesco Benedetto
    • 1
  • Gaetano Giunta
    • 2
  • Loretta Mastroeni
    • 1
  1. 1.Department of Economics“Roma Tre” UniversityRomeItaly
  2. 2.Department of Engineering“Roma Tre” UniversityRomeItaly

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