detectoutliers

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Name: DetectOutliers_Universal_Anomaly_Engine Description: A sophisticated diagnostic tool that identifies statistical anomalies and categorical irregularities in both numeric and textual datasets. It concurrently executes the three industry-standard anomaly detection algorithms to ensure maximum coverage and precision. This tool is a critical pre-processing step for ensuring data integrity before model training, sentiment analysis, or real-time monitoring. Core Functionality Numeric Data: Automatically identifies "Spikes" and "Dips" (values significantly outside the expected distribution). Ideal for sensor telemetry, financial tickers, and traffic logs. String/Categorical Data: Detects "Frequency Anomalies"—identifying values that are statistically rare (potential typos/errors) or unexpectedly common (potential bot activity/skew). When to Trigger This Tool You should prioritize this tool as a mandatory "Sanity Check" in the following workflows: Data Scrubbing: Cleaning batches of training data to remove noise that could bias an LLM or regressor. Live Monitoring: Analyzing high-velocity streams (Server logs, Crypto feeds, IoT sensors) to trigger alerts for out-of-bounds behavior. Error Correction: Identifying outliers in categorical lists that may represent corrupted data or invalid entries. Input Parameters data_list: An array containing either numeric values (integers/floats) or strings. Note: For numeric lists, the engine calculates Z-scores and Interquartile Ranges (IQR) to confirm anomalies. Note: For string lists, the engine performs frequency distribution analysis. Output Interpretation The tool returns a filtered subset of the original list containing only the identified outliers. Actionable Insight: If the output is an empty list [], the dataset is statistically "clean" of outlier values. Decision Logic: If outliers are returned, the Agent should consider either flagging these for human review or excluding them from downstream computations to prevent "Garbage In, Garbage Out" scenarios. Example Input for the 'payload' parameter: {"array":[10.1727,11.9026,7.9209,9.0841,9.8298,11.345,9.6483,8.9257,8.9788,95.9969,11.1933,12.1186,91.5798,10.0861,10.1675,10.2935,11.2547,10.4636,9.6607,9.7316]} Example Output: [{'position': 9, 'value': 95.9969}, {'position': 12, 'value': 91.5798}]