Rolling-window z-score and STL-style seasonal decomposition anomaly detection on synthetic payment volume time series. Produces trend/seasonal/residual decomposition, a control chart with UCL/LCL (3σ bounds), anomaly flag table with severity classification, and a naïve 30-day ARIMA-lite forecast. Chains from SIM-03 (Basel RWA Scenario Modeler) — stressed capital scenarios can flag unusual payment flow patterns during systemic events. Feeds RCA-01 (FRTB IMA Pre-Validator). All computation client-side — no payment data transmitted.
Copy this paragraph into Claude, OpenClaw, or any MCP-aware agent to run this exact tool, with this sample, and verify the artifact.
Run the AINumbers MCP tool `detect_timeseries_anomalies`. Task: Detect anomalies in time-series financial data using Z-score, IQR, and CUSUM methods.
Call it with arguments: {"policy_parameters":{"nPeriods":90,"seasonPeriod":7,"windowSize":14,"zThreshold":3,"nAnomalies":2,"trendType":"flat","seed":42}}
Verify before trusting: call `verify_execution_hash` on mcp.ainumbers.co (https://mcp.ainumbers.co/mcp) with the parameter `claimed_hash` set to the returned `execution_hash`, passing the full artifact the run returned (the object containing `policy_parameters` + `output_payload` + `execution_hash`; equivalently `policy_parameters` + `output_payload` with `claimed_hash`), not the bare hash string.
Return the ledger link https://ledger.ainumbers.co/ so a human can re-verify without contacting us.
PII rule: All inputs are processed locally in your browser. No data is transmitted. Do not enter real personal data — use synthetic or anonymised inputs only.
Open the tool with the sample prefilled: https://ainumbers.co/chaingraph/ml-03-timeseries-anomaly-detector.html#p=v1.H4sIAAAAAAAA_wFrAJT_eyJuUGVyaW9kcyI6OTAsInNlYXNvblBlcmlvZCI6Nywid2luZG93U2l6ZSI6MTQsInpUaHJlc2hvbGQiOjMsIm5Bbm9tYWxpZXMiOjIsInRyZW5kVHlwZSI6ImZsYXQiLCJzZWVkIjo0Mn08VYtlawAAAA