Automated Time Series Anomaly Detection with EWMA Control Charts

Implement EWMA (Exponentially Weighted Moving Average) control charts in Excel to detect anomalies in time series metrics. Compute EWMA with a chosen smoothing factor (lambda), estimate control limits based on process variance, and flag points outside limits as anomalies. Complement with rolling z-score and median absolute deviation for robustness to outliers. Create a dashboard with anomaly table showing date, value, z-score, and suggested labels (spike/drop). For frequent or high-volume signals, add logic to suppress repeated alerts within a cooldown window. This is practical for monitoring product metrics, site performance, or revenue streams inline with alert thresholds before escalating.