Refining Heisenberg’s principle: A greedy approximation of step functions with triangular waveform dictionaries DOI Creative Commons
Alessandro Mazzoccoli, Jorge Andres Rivero, Pierluigi Vellucci

et al.

Mathematics and Computers in Simulation, Journal Year: 2024, Volume and Issue: 225, P. 165 - 176

Published: May 18, 2024

In this paper, we consider a step function characterized by real-valued sequence and its linear expansion representation constructed via the matching pursuit (MP) algorithm. We utilize waveform dictionary based on triangular as part of algorithm representation. The is comprised waveforms localized in time-frequency domain. view this, prove that are more efficient than rectangular used prior study achieving product variances domain closer to lower bound Heisenberg Uncertainty Principle. provide MP solvable polynomial time, contrasting common exponential time when using Gaussian windows. apply simulated data real GDP from 1947–2024 demonstrate application efficiency.

Language: Английский

Wavelet entropy and complexity–entropy curves approach for energy commodity price predictability amid the transition to alternative energy sources DOI Creative Commons
Loretta Mastroeni, Alessandro Mazzoccoli, Pierluigi Vellucci

et al.

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 184, P. 115005 - 115005

Published: May 23, 2024

In recent years, energy commodities have emerged as pivotal and widely debated subjects, driven by their profound influence on the global economy intricate interconnections. Moreover, challenges stemming from predictability of commodity prices become a prominent intensifying focus discussion. To this aim, in paper, we employ wavelet analysis with an entropy approach to investigate evaluate fluctuations, low-frequency events, rare events consequent such time series. particular, can differentiate high-frequency movements series, is valuable mathematical tool used state disorder, randomness, or uncertainty Specifically, Rényi Entropy instead Shannon because it allows for enhanced consideration spikes Therefore, analyze use Wavelet (WRE) complexity–entropy curves combining transform entropy. Finally, apply our real financial data, including indices that describe transition alternative resources.

Language: Английский

Citations

6
Geopolitical risk and uncertainty in energy markets: Evidence from wavelet-based methods DOI Creative Commons

Ivan De Crescenzo,

Loretta Mastroeni,

Greta Quaresima

et al.

Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108281 - 108281

Published: Feb. 1, 2025

Language: Английский

Citations

0
Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty DOI Creative Commons
Lyubov Doroshenko, Loretta Mastroeni, Alessandro Mazzoccoli

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(8), P. 1346 - 1346

Published: April 20, 2025

The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics guiding decision-making. Financial uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses energy-based measures to investigate relationship between these prices across multiple time scales. approach captures complex, time-varying dependencies, offering a more nuanced how influence fluctuations. By integrating this with predictability measures, we assess enhance forecasting accuracy. We further incorporate deep learning models capable capturing sequential patterns financial series into our better evaluate their predictive potential. Our findings highlight varying impact on prices, showing while some offer information, others display strong correlations without significant power. These results underscore need tailored models, as different commodities react differently same conditions. combining wavelet-based machine techniques, presents comprehensive framework evaluating role markets. gained can support investors, policymakers, market analysts making informed decisions.

Language: Английский

Citations

0
Kaldor–Kalecki Business Cycle Model: An 80-Year Multidisciplinary Retrospective DOI
Giuseppe Orlando, Pierluigi Vellucci, Giovanna Zimatore

et al.

Nonlinear Dynamics, Journal Year: 2025, Volume and Issue: unknown

Published: May 6, 2025

Language: Английский

Citations

0
Quantifying predictive knowledge: Wavelet energy α-divergence measure for time series uncertainty reduction DOI
Loretta Mastroeni, Alessandro Mazzoccoli

Chaos Solitons & Fractals, Journal Year: 2024, Volume and Issue: 188, P. 115488 - 115488

Published: Sept. 6, 2024

Language: Английский

Citations

2
Effects of the climate-related sentiment on agricultural spot prices: Insights from Wavelet Rényi Entropy analysis DOI
Loretta Mastroeni, Alessandro Mazzoccoli,

Greta Quaresima

et al.

Energy Economics, Journal Year: 2024, Volume and Issue: unknown, P. 108146 - 108146

Published: Dec. 1, 2024

Language: Английский

Citations

2
Refining Heisenberg’s principle: A greedy approximation of step functions with triangular waveform dictionaries DOI Creative Commons
Alessandro Mazzoccoli, Jorge Andres Rivero, Pierluigi Vellucci

et al.

Mathematics and Computers in Simulation, Journal Year: 2024, Volume and Issue: 225, P. 165 - 176

Published: May 18, 2024

In this paper, we consider a step function characterized by real-valued sequence and its linear expansion representation constructed via the matching pursuit (MP) algorithm. We utilize waveform dictionary based on triangular as part of algorithm representation. The is comprised waveforms localized in time-frequency domain. view this, prove that are more efficient than rectangular used prior study achieving product variances domain closer to lower bound Heisenberg Uncertainty Principle. provide MP solvable polynomial time, contrasting common exponential time when using Gaussian windows. apply simulated data real GDP from 1947–2024 demonstrate application efficiency.

Language: Английский

Citations

2