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University of Salento

Academic institutioneurope · it
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Research library16linked papers
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Selected work

Representative Papers

Fast and Accurate Anomaly Detection in Time Series

Jul 02, 2026

This study addresses the challenges in time series anomaly detection posed by extreme class imbalance and scarce labeled data, which hinder supervised approaches and lead to high false positive rates in unsupervised methods. To overcome these limitations, the authors propose an unsupervised detection framework that integrates Haar discrete wavelet transform with a tailored t-test. By decomposing the signal across multiple scales and applying statistically grounded significance testing, the method effectively identifies anomalies without requiring labeled data. This work is the first to synergistically combine Haar wavelets with theoretically justified t-tests, substantially reducing false positives while enhancing detection accuracy. Extensive experiments on 343 real-world datasets demonstrate that the proposed approach outperforms current state-of-the-art unsupervised and self-supervised methods in both detection speed and accuracy.

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From Classical to Quantum-Mechanical Data Assimilation: A Comparison between DATO and QMDA

May 06, 2026

Data assimilation provides a systematic framework for combining dynamical models with partial and noisy observations to infer the evolving state of a system. In this work, we undertake a comparative study of Data Assimilation with Transfer Operators (DATO) and Quantum Mechanical Data Assimilation (QMDA), focusing on their mathematical formulation, algorithmic structure, and empirical performance. Both methods are first cast within a common operator-theoretic framework, which makes it possible to compare, on a unified basis, their representations of uncertainty, forecast propagation, and assimilation updates. We then analyse their principal similarities and differences with respect to state-space structure, update mechanisms, structural preservation properties, and computational cost. To complement the theoretical analysis, we assess both approaches on benchmark dynamical systems across a range of observational settings, including noisy, sparse, and partially observed regimes. Our results show that, despite their shared operator-theoretic motivation, DATO and QMDA embody substantially different assimilation paradigms, leading to distinct advantages and limitations in terms of interpretability, robustness, and scalability. The present study helps delineate the regimes in which each framework is most effective and offers broader insight into the design of operator-based methodologies for data assimilation.

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Recent publications

Latest Papers

Fast and Accurate Anomaly Detection in Time Series

Jul 02, 2026

This study addresses the challenges in time series anomaly detection posed by extreme class imbalance and scarce labeled data, which hinder supervised approaches and lead to high false positive rates in unsupervised methods. To overcome these limitations, the authors propose an unsupervised detection framework that integrates Haar discrete wavelet transform with a tailored t-test. By decomposing the signal across multiple scales and applying statistically grounded significance testing, the method effectively identifies anomalies without requiring labeled data. This work is the first to synergistically combine Haar wavelets with theoretically justified t-tests, substantially reducing false positives while enhancing detection accuracy. Extensive experiments on 343 real-world datasets demonstrate that the proposed approach outperforms current state-of-the-art unsupervised and self-supervised methods in both detection speed and accuracy.

0 citationsRead paper

From Classical to Quantum-Mechanical Data Assimilation: A Comparison between DATO and QMDA

May 06, 2026

Data assimilation provides a systematic framework for combining dynamical models with partial and noisy observations to infer the evolving state of a system. In this work, we undertake a comparative study of Data Assimilation with Transfer Operators (DATO) and Quantum Mechanical Data Assimilation (QMDA), focusing on their mathematical formulation, algorithmic structure, and empirical performance. Both methods are first cast within a common operator-theoretic framework, which makes it possible to compare, on a unified basis, their representations of uncertainty, forecast propagation, and assimilation updates. We then analyse their principal similarities and differences with respect to state-space structure, update mechanisms, structural preservation properties, and computational cost. To complement the theoretical analysis, we assess both approaches on benchmark dynamical systems across a range of observational settings, including noisy, sparse, and partially observed regimes. Our results show that, despite their shared operator-theoretic motivation, DATO and QMDA embody substantially different assimilation paradigms, leading to distinct advantages and limitations in terms of interpretability, robustness, and scalability. The present study helps delineate the regimes in which each framework is most effective and offers broader insight into the design of operator-based methodologies for data assimilation.

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