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This PR introduces a
ZScore-basedanomaly detection model. The detector operates on pandas DataFrames representing time series data and computes the z-score for each column independently. For each series, it identifies and flags the observations with the highest and lowest z-scores as anomalies.The implementation includes safeguards for edge cases such as empty inputs, all-NaN columns, and constant series, ensuring that anomaly detection is skipped safely when z-score computation is not meaningful.
Function decorator is added according to PR #67.
Model capabilities are added according to PR #69.
fyi @sarusso
fyi @clarasaja