ordpy: A Python package for data analysis with permutation entropy and ordinal network methods

AAB Pessa, HV Ribeiro - Chaos: An Interdisciplinary Journal of …, 2021 - pubs.aip.org
Since Bandt and Pompe's seminal work, permutation entropy has been used in several
applications and is now an essential tool for time series analysis. Beyond becoming a …

Ordinal methods: Concepts, applications, new developments, and challenges—In memory of Karsten Keller (1961–2022)

JM Amigó, OA Rosso - Chaos: An Interdisciplinary Journal of Nonlinear …, 2023 - pubs.aip.org
In 2013, Karsten Keller, Jürgen Kurths, and one of us (JMA) guest edited an issue of the
European Physical Journal Special Topics, entitled Recent Progress in Symbolic Dynamics …

Generation of individualized synthetic data for augmentation of the type 1 diabetes data sets using deep learning models

J Noguer, I Contreras, O Mujahid, A Beneyto, J Vehi - Sensors, 2022 - mdpi.com
In this paper, we present a methodology based on generative adversarial network
architecture to generate synthetic data sets with the intention of augmenting continuous …

Assessment of sector bond, equity indices and green bond index using information theory quantifiers and clusters techniques

LHS Fernandes, FHA De Araujo, JWL Silva… - Fractals, 2023 - World Scientific
Green bonds are financial assets similar to classic debt securities used to finance
sustainable investments. Given this, they are a long-term investment alternative that …

Scientific progress in information theory quantifiers

AMF Martins, LHS Fernandes… - Chaos, Solitons & Fractals, 2023 - Elsevier
Abstract The Bandt and Pompe method (BPM) has been successfully applied to estimate the
information theory quantifiers. The most significant limitation of the BPM is that this approach …

Rényi entropy-complexity causality space: A novel neurocomputational tool for detecting scale-free features in EEG/iEEG data

N Guisande, F Montani - Frontiers in Computational Neuroscience, 2024 - frontiersin.org
Scale-free brain activity, linked with learning, the integration of different time scales, and the
formation of mental models, is correlated with a metastable cognitive basis. The spectral …

Continuous imputation of missing values in time series via Wasserstein generative adversarial imputation networks and variational auto-encoders model

Y Wang, X Xu, L Hu, J Liu, X Yan, W Ren - Physica A: Statistical Mechanics …, 2024 - Elsevier
The occurrence of missing values in time series is a common phenomenon attributed to
equipment malfunction during data acquisition and transmission errors. However, most …

Quantifying the diversity of multiple time series with an ordinal symbolic approach

L Zunino, MC Soriano - Physical Review E, 2023 - APS
The main motivation of this paper is to introduce the ordinal diversity, a symbolic tool able to
quantify the degree of diversity of multiple time series. Analytical, numerical, and …

Wear diagnosis for rail profile data using a novel multidimensional scaling clustering method

D Shang, S Su, YK Sun, F Wang, Y Cao… - … ‐Aided Civil and …, 2024 - Wiley Online Library
The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The
rail surface wear is the key impact factor when considering the health conditions of rails. This …

A generative adversarial network to speed up optical Monte Carlo simulations

C Trigila, A Srikanth, E Roncali - Machine Learning: Science and …, 2023 - iopscience.iop.org
Detailed simulation of optical photon transport and detection in radiation detectors is often
used for crystal-based gamma detector optimization. However, the time and memory burden …