Intrinsic predictability is imperative to quantify inherent information contained in a time series and assists in evaluating the performance of different forecasting methods to get the best possible prediction. Model forecasting performance is the measure of the probability of success. Nevertheless, model performance or the model does not provide understanding for improvement in prediction. Intuitively, intrinsic predictability delivers the highest level of predictability for a time series and informative in unfolding whether the system is unpredictable or the chosen model is a poor choice. We introduce a novel measure, the Wavelet Entropy Energy Measure (WEEM), based on wavelet transformation and information entropy for quantification of intrinsic predictability of time series. To investigate the efficiency and reliability of the proposed measure, model forecast performance was evaluated via a wavelet networks approach. The proposed measure uses the wavelet energy distribution of a time series at different scales and compares it with the wavelet energy distribution of white noise to quantify a time series as deterministic or random. We test the WEEM using a wide variety of time series ranging from deterministic, non-stationary, and ones contaminated with white noise with different noise-signal ratios. Furthermore, a relationship is developed between the WEEM and Nash–Sutcliffe Efficiency, one of the widely known measures of forecast performance. The reliability of WEEM is demonstrated by exploring the relationship to logistic map and real-world data.
Wavelet entropy-based evaluation of intrinsic predictability of time series
Note: This article is part of the Focus Issue, Rare Events in Complex Systems: Understanding and Prediciton.
Ravi Kumar Guntu, Pavan Kumar Yeditha, Maheswaran Rathinasamy, Matjaž Perc, Norbert Marwan, Jürgen Kurths, Ankit Agarwal; Wavelet entropy-based evaluation of intrinsic predictability of time series. Chaos 1 March 2020; 30 (3): 033117. https://doi.org/10.1063/1.5145005
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