Add single-series anomaly detection#309
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data_drift is two-batch distribution shift and slo.burn_alerts only thresholds budget burn — neither points at which value in one live series is anomalous. Add detect_anomalies (mad/zscore scored records), mad_anomalies / zscore_anomalies, mad_scores / zscore_scores, and an ewma_control chart with an optional in-control baseline. Wired through facade, executor (AC_detect_anomalies), MCP, and the Script Builder with a headless test batch and EN/Zh docs.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 46 |
| Duplication | 0 |
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What
data_driftis two-batch distribution shift andslo.burn_alertsonly thresholds budget burn — neither points at which value in one live series is anomalous.detect_anomalies(values, *, method="mad", threshold=None)→[{index, value, score, is_anomaly}].mad_anomalies/zscore_anomalies(flagged indices);mad_scores/zscore_scores(raw scores).ewma_control(values, *, alpha, limit, target_mean, target_sigma)— EWMA control chart for sustained shifts.MAD = robust Iglewicz-Hoaglin modified z-score. Pairs with
timeseries.Layers
utils/anomaly/(pure stdlibmath/statistics, zero PySide6).__all__.AC_detect_anomalies.ac_detect_anomalies(read-only).test/unit_test/headless/test_anomaly_batch.py(9 tests).v101_features_doc.rst(EN + Zh) + toctrees + 3 README What's-new sections.Verification
pytest test/unit_test/headless/test_anomaly_batch.py→ 9 passed.ruff check je_auto_control/clean; pylint 10.00/10; bandit clean; radon CC clean.