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docs/source/user_guide/operators/anomaly_detection_operator/index.rst

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**Input Data**
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The Anomaly Detection Operator accepts a dataset with:
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# A datetime column
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# A target column
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# (Optionally) 1 or more seires columns (such that the target is indexed by datetime and series)
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# (Optionall) An arbitrary number of additional variables
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* A datetime column
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* A target column
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* (Optionally) 1 or more seires columns (such that the target is indexed by datetime and series)
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* (Optionall) An arbitrary number of additional variables
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Besides this input data, the user can also specify validation data, if available. Validation data should have all the columns of the input data plus a binary column titled "anomaly". The "anomaly" column should be -1 for anomalies and 1 for normal rows.
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docs/source/user_guide/operators/anomaly_detection_operator/use_cases.rst

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* The datetime input column must have a consistent interval throughout the historical and additional datasets. Inconsistent diffs will cause failure on automlx and may affect performance on other frameworks.
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* It is recommended that the datetime column is passed in sorted from earliest to latest, if not, the operator will sort on your behalf.
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* It is recommended that you pass in the format of your datetime string into the ``format`` option of the ``datetime_column`` parameter. The operator uses the python datetime string format outlined here: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes
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* For example "31/02/2024" would be "%m/%d/%Y". "July 24" would be "%B %y".
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* It is recommended that you pass in the format of your datetime string into the ``format`` option of the ``datetime_column`` parameter. The operator uses the python datetime string format outlined here: https://docs.python.org/3/library/datetime.html#strftime-and-strptime-format-codes (For example "31/02/2024" would be "%m/%d/%Y". "July 24" would be "%B %y".)
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**Validation Data**

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