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.
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Benedetto, F., Giunta, G., Mastroeni, L. (2016). A Computational Method for Predicting the Entropy of Energy Market Time Series. In: Fonseca, R., Weber, GW., Telhada, J. (eds) Computational Management Science. Lecture Notes in Economics and Mathematical Systems, vol 682. Springer, Cham. https://doi.org/10.1007/978-3-319-20430-7_6
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DOI: https://doi.org/10.1007/978-3-319-20430-7_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20429-1
Online ISBN: 978-3-319-20430-7
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