Holt-Winters smoother/forecaster for anomaly detection
Forecast threads a smooth curve through a noisy timeseries in a way that lets you visualize trends, cycles, and anomalies. It can be used as part of an automatic anomaly detection system for metric timeseries (that is, sequences of timestamped numerical values).
It accomplishes this using a variation of Holt-Winters forecasting -- more generally known as exponential smoothing. Forecast decomposes a noisy signal into level, trend, repetitive "seasonal" effects, and unexplained variation or noise. In this example, a "season" is a day long, and we model repeated day-over-day variation. The result is a smoothed version of the signal which may be used to forecast future values or detect unexpected variation. This approach has been successfully used for [network anomaly detection](https://www.usenix.org/legacy/event/lisa2000/full_papers/br