Learning Penalty for Changepoint Detection via Automatic Feature Extraction
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
skchange is an open‑source Python library that provides fast and flexible algorithms for detecting structural changes in time series. It offers modular, composable methods based on cost minimisation and statistical tests, and includes features such as anomalous segment detection, high‑dimensional data support, automatic penalty calibration, and a wide range of built‑in costs and tests. The library follows scikit‑learn conventions and uses Numba for high computational performance, with source code and documentation available on GitHub.
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arXiv:2608. 19767v1 Announce Type: cross Abstract: Skchange is an open-source Python library for detecting structural changes in time series.
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SENTRY is a deterministic risk assessment platform for IT change management that replaces subjective questionnaires with a machine‑learning pipeline using XGBoost and retrieval‑augmented generation. It integrates structured metadata, dependency graphs, incident history, and a hybrid semantic‑lexical search of change request text, compressing the unstructured signal into a single feature for model inference. The system delivers a ROC AUC of 0.87, 85% accuracy, and detects high‑risk changes 3.25 times faster than existing processes.