Entropy is a powerful tool for the analysis of time series, as it allows describing the
probability distributions of the possible state of a system, and therefore the information …

More than ten years ago Bandt and Pompe introduced a new measure to quantify
complexity in measured time series. During these ten years, this measure has been modified …

This paper presents a novel hybrid model for fault detection and classification of motor
bearing. In the proposed model, permutation entropy (PE) of the vibration signal is …

Permutation entropy (PE) has been recently suggested as a novel measure to characterize
the complexity of nonlinear time series. In this paper, we propose a simple method to …

Financial time series forecasting is a popular application of machine learning methods.
Previous studies report that advanced forecasting methods predict price changes in financial …

Since its inception, the digital currency market is considerably growing, especially in the
most recent years. The main purpose of this paper is to investigate, assess and detect …

Bearing fault diagnosis has attracted significant attention over the past few decades. It
consists of two major parts: vibration signal feature extraction and condition classification for …

One of the most important aspects of time series is their degree of stochasticity vs. chaoticity.
Since the discovery of chaotic maps, many algorithms have been proposed to discriminate …

We divided the whole series of Shenzhen stock market into two sub-series at the criterion of
the date of a reform and their scale behaviors are investigated using multifractal detrended …

The complexity-entropy causality plane has been recently introduced as a powerful tool for
discriminating Gaussian from non-Gaussian process and different degrees of correlations …