waveguard_scan_timeseries
ActiveTool of WaveGuard
Detect anomalies in time-series data — use after pulling numeric metrics from monitoring APIs, financial data sources, IoT sensors, or spreadsheet columns. Send a single numeric array and specify a window size. Early windows define 'normal', recent windows are tested for anomalies. Typical workflow: (1) Pull a column of numbers from Sheets, a Supabase time-series table, or a metrics API. (2) Pass the array here. (3) Get back which time windows are anomalous. Examples: - Revenue monitoring: Pull monthly revenue from Sheets → detect anomalous months - Stock screening: Pull 90 days of closing prices → find unusual price windows - Server health: Pull response-time metrics → identify degradation windows - Sensor QA: Pull temperature readings from IoT API → flag sensor drift
Parameters schema
{
"type": "object",
"required": [
"data"
],
"properties": {
"data": {
"type": "array",
"items": {
"type": "number"
},
"minItems": 6,
"description": "Numeric time-series array, ordered chronologically. Should have at least 3x window_size data points."
},
"sensitivity": {
"type": "number",
"description": "Anomaly sensitivity (default: 1.0). Higher = more sensitive."
},
"window_size": {
"type": "integer",
"default": 10,
"minimum": 2,
"description": "Number of data points per window (default: 10). Smaller windows detect finer-grained anomalies."
},
"test_windows": {
"type": "integer",
"minimum": 1,
"description": "Number of most recent windows to test (default: half of total windows). The rest are used as training (normal baseline)."
}
}
}No endpoints wrapped at confidence ≥ 0.70.
Parent server
WaveGuard
https://github.com/gpartin/LFMAnomalyDetection
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