ActiveLearning4SPM
Active Learning for Process Monitoring
Description
Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly.
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Changelog
Full NEWS →v0.1.0
Initial release:
•
simulate_stream() generates simulates data to perform stream-based active learning for process monitoring, as shown in the simulation study of Capezza, Lepore and Paynabar (2025).•
fit_pHMM() fits a partially hidden Markov model (pHMM) to the data, as described in Capezza, Lepore and Paynabar (2025).•
fit_pHMM_auto() fits a pHMM to the data with automatic selection of the number of hidden states and with automatic initialization, as shown in Capezza, Lepore and Paynabar (2025).•
active_learning_pHMM() performs stream-based active learning for process monitoring, as described in Capezza, Lepore and Paynabar (2025).References:
• Capezza, C., Lepore, A., & Paynabar, K. (2025). Stream-Based Active Learning for Process Monitoring. Technometrics. <doi:10.1080/00401706.2025.2561744>.