The 9 ML Anomaly Detection Methods ThresholdIQ Uses — Explained in Plain English

When you upload a spreadsheet to ThresholdIQ , nine separate machine learning methods run simultaneously across every column in your data. Each one is looking for a different type of problem. Some catch sudden spikes. Others find slow drift. One looks for sensors that have frozen. Another watches for two metrics that normally move together suddenly moving apart. Most people don't need to know how any of this works — they just want the anomaly flagged. But if you've ever wondered "why did **ThresholdIQ **flag that?" or "what would it miss?", this guide is for you. Each method gets a plain-English explanation, a concrete worked example, and an honest summary of what it catches and what it doesn't. Contents Multi-Window Z-Score — the primary severity driver EWMA Spike Detection — sudden event catcher SARIMA Seasonal Residuals — seasonality-aware detection Isolation Forest — multivariate outlier detection Correlation Deviation — correlated failure detection DBSCAN Cluster Noise — behaviour