Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time-series analysis

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 effectively contributes to the planet’s future by preserving the environment and encouraging sustainable development. This research encompasses a rich dataset of equity and bond sectors, general indices, and the S&P Green Bond Index. We estimate the permutation entropy [Formula: see text], an appropriate statistical complexity measure [Formula: see text], and Fisher Information measure [Formula: see text]. Therefore, we employ these complexity measures to construct two 2D maps, the complexity-entropy causality plane ([Formula: see text] ×[Formula: see text]) and the Shannon–Fisher causality plane ([Formula: see text] ×[Formula: see text]). Also, we use the information theory quantifiers to rank these indices’ efficiency analogous to the complexity hierarchy. From a mathematical point of view, the complexity-entropy causality plane (CECP) is a map that considers the global analysis, while the SFCP is a map that simultaneously feels the global and local analysis. Our findings reveal that both 2D maps indicated the most efficient (b_info_tech) and least efficient (b_energy) assets. There are peculiarities in the ranking performed considering the information theory quantifiers used to build each map due to the mathematical distinction that underlies the construction of each map. Moreover, we applied two clustering approaches ([Formula: see text]-means and Hierarchical cluster) that categorically converged in the indication of four distinct groups, which allowed us to verify that, in an overview, equities present a unique dynamic when compared to bonds and the Green bond index.

show abstract

“…While the permutation entropy uses ordinal patterns to compare neighbouring values of each point and map them [29,30,31].…”

Section: Permutation Entropymentioning

confidence: 99%

Assessment of Sector Bond, Equity Indices and Green Bond Index Using Information Theory Quantifiers and Clusters Techniques

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Distributions were compared by quantifying the similarity (S) between a normalized generated (h G ) and simulated (h R ) optical photons distribution at the photodetector face using the Jensen-Shannon distance (JSD), a similarity measure of probability distributions (Menéndez et al 1997, Zunino et al 2022. The JSD represents the statistical distance between two distributions and is given by equation ( 4):…”

Section: Quantitative Comparisonmentioning

confidence: 99%

A generative adversarial network to speed up optical Monte Carlo simulations

Trigila

Srikanth

Roncali

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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 associated with the track-wise approach to particle transport and detection in commonly used Monte Carlo codes makes optical simulation prohibitive at a system level, where hundreds to thousands of scintillators must be modeled. Consequently, current large system simulations do not include detailed detector models to analyze the potential performance gain with new radiation detector technologies. Generative Adversarial Networks are explored as a tool to speed up the optical simulation of crystal-based detectors. These networks learn training datasets made of high-dimensional data distributions. Once trained, the resulting model can produce distributions belonging to the training data probability distribution. In this work, we present the proof of concept of using a Generative Adversarial Network to enable high-fidelity optical simulations of nuclear medicine systems, mitigating their computational complexity. The architecture of the first network version and high-fidelity training dataset is discussed. The latter is generated through accurate optical simulation with GATE/Geant4, and contains the position, direction, and energy distributions of the optical photons emitted by 511 keV gamma rays in bismuth germanate and detected on the photodetector face. We compare the GAN and simulation-generated distributions in terms of similarity using the Jensen-Shannon distance. Excellent agreement was found with similarity values higher than 93.5% for all distributions. Moreover, the GAN speeded the optical photon distribution generation by up to 2 orders of magnitude. These very promising results have the potential to drastically change the use of nuclear imaging system optical simulations by enabling high-fidelity system-level simulations in reasonable computation times. The ultimate is to integrate the GAN within GATE/Geant4 since numerous applications (large detectors, bright scintillators, Cerenkov-based timing positron emission tomography) can benefit from these improvements.

show abstract

“…The use of Jensen-Shannon divergence to evaluate the distance between two time series has also been investigated to some degree [39,40]. Originally, Jensen-Shannon divergence is used to quantify the distance between two probability distributions.…”

Section: Similarity Measuresmentioning

confidence: 99%

Automated Parameter Tuning with Accuracy Control for Efficient Reservoir Simulations

Saeternes,

Thune,

Rustad

et al. 2023

Preprint

View full text Add to dashboard Cite

Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time-series analysis

Cited by 26 publications

References 87 publications

Assessment of Sector Bond, Equity Indices and Green Bond Index Using Information Theory Quantifiers and Clusters Techniques

Assessment of Sector Bond, Equity Indices and Green Bond Index Using Information Theory Quantifiers and Clusters Techniques

A generative adversarial network to speed up optical Monte Carlo simulations

Automated Parameter Tuning with Accuracy Control for Efficient Reservoir Simulations

Contact Info

Product

Resources

About