While using Prophet for purely "forecasting" setup might not guarantee consistent high-quality results out of the box, especially for noisy and complicated time

While using Prophet for purely "forecasting" setup might not guarantee consistent high-quality results out of the box, especially for noisy and complicated time series data, at VictoriaMetrics we found it practically useful for anomaly detection task: In our vmanomaly product, Prophet is one of the go-to models for anomaly detection in metrics data and it usually requires little tuning to achieve considerable results. The main purpose for the use of Prophet or similar forecasting models is to reformulate the task of anomaly detection: - given fitted model M, ground truth Y_i for particular data point X_i, we produce forecast Yhat_i and its uncertainty estimate [Yhat_lb, Yhat_ub] - if ground truth Y_i falls beyond the range of [Yhat_lb, Yhat_ub], we consider this point an anomaly - the further Y_i is from the range, the higher the anomaly score would be. In our particular implementation for easier alerting purposes, anomaly_score > 1 means "anomaly" here's a small visual example: https://docs.victoriametrics.com/vmanomaly.html#examples https://docs.victoriametrics.com/vmanomaly.html#examples