Studying the impact of fluctuations, spikes and rare events in time series through a wavelet entropy predictability measure

Loretta Mastroeni, Alessandro Mazzoccoli and Pierluigi Vellucci

Physica A: Statistical Mechanics and its Applications, 2024, vol. 641, issue C

Abstract: Data has become one of the most crucial sources of human life. In particular, the ability to predict the future through data is a widely studied topic. In finance, as an instance, increased volatility, fluctuations, low-frequency events, and rare events negatively affect the predictability of data, thus increasing the level of risk. As a consequence, the inability to make accurate predictions on future events increases the uncertainty and variability of a given scenario, indicating a consequent increase in risk. In this paper, we analyze data predictability introducing a new measure based on entropy and the wavelet transform. In particular, we show that the data are less predictable than one might expect due to the mentioned fluctuations and low-frequency events. Furthermore, we apply our tool to real data, in particular to time series of commodities. As a result, thanks to this new measure, we can observe that the price time series under analysis exhibit a significant level of unpredictability due to increased volatility, fluctuations, and the influence of low-frequency events.

Keywords: Wavelet; Entropy; Predictability; Time series (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:641:y:2024:i:c:s0378437124002292

DOI: 10.1016/j.physa.2024.129720

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