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Published: June 22, 2026

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Published: June 22, 2026

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publications

Time Series Step Tree: A Novel Interpretable Method for Prompt Classification of Time Series

Published in 8th International Conference on the Dynamics of Information Systems (DIS 2025), 2026

A novel interpretable method for the prompt classification of time series.

Recommended citation: Zare, O., Sala, P., Amadori, D., Chini, E., Joloudari, J.H. (2026). "Time Series Step Tree: A Novel Interpretable Method for Prompt Classification of Time Series." 8th International Conference on the Dynamics of Information Systems (DIS 2025). https://link.springer.com/chapter/10.1007/978-3-032-16469-8_14

DRIFTS: Distributed Robustness for Time Series is Feasible, Distributed, and Fun

Published in IEEE Conference on Artificial Intelligence (IEEE CAI26), 2026

A distributed approach to robustness verification for time series classification.

Recommended citation: Amadori, D., Chini, E., Sala, P. (2026). "DRIFTS: Distributed Robustness for Time Series is Feasible, Distributed, and Fun." IEEE Conference on Artificial Intelligence (IEEE CAI26). https://ieeexplore.ieee.org/document/11536523

PACO: A Petri Net-Based Tool for Designing, Simulating, and Analyzing Multi-Objective Stochastic Processes

Published in Application and Theory of Petri Nets and Concurrency (PETRI NETS 2026), LNCS vol. 16567, pp. 335-346, 2026

A Petri Net-based tool for designing, simulating, and analyzing multi-objective stochastic processes.

Recommended citation: Chini, E., Amadori, D., Sala, P., Nasir Rajput, S., Baldi, M., Cappelletti, M. (2026). "PACO: A Petri Net-Based Tool for Designing, Simulating, and Analyzing Multi-Objective Stochastic Processes." Application and Theory of Petri Nets and Concurrency (PETRI NETS 2026), LNCS vol. 16567, pp. 335-346. https://link.springer.com/chapter/10.1007/978-3-032-27879-1_16