Maximum entropy estimator for the predictability of energy commodity market time series

This paper proposes a novel method for assessing the predictability of energy market time series, by predicting the entropy of the series. According to conventional entropy-based analysis where the entropy is always ex-post estimated), high entropy values characterize unpredictable series, while more stable series exhibits lesser entropy values. Here, we predict ex-ante 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. Preliminary results, applied to energy commodities, show the efficacy of the proposed method for application to energy market time series.