How to build a live time series anomaly detection model

A live time series anomaly detection model can help detect irregularities in any metric within your data stack. This type of model uses statistical models on the most recent data to flag inconsistencies, allowing for early identification and resolution of potential problems. Time series models are particularly effective as they handle trends, cycles, and other common patterns in data. By learning from historical data, these models can adapt to changing conditions and provide valuable insights into anomalies within your metrics.