Anomaly Detection

Using Tangent for anomaly detection is straightforward—here’s how it works:

What You Need:

• Time Series/IoT Data: The raw data that reflects the behavior you want to monitor.

• Configuration: A setup that defines how Tangent should process the data.

How It Works:

1. Generate a Normal Behavior Model:

   • Tangent uses your time series data and configuration to create a model of normal behavior over an extended period. This model captures what is considered typical or expected within the data.

2. Detect Anomalies:

   • Once the normal behavior model is established, you can use it to detect anomalies during an out-of-sample period. This involves identifying any deviations from the established normal behavior, whether they are structural (consistent patterns) or sporadic (isolated incidents).

Key Differences from Forecasting:

• Static vs. Dynamic:

  • In anomaly detection, the model for normal behavior remains static for longer time periods—it’s built over time to encompass all possible normal conditions. This helps in spotting deviations effectively.

  • In contrast, forecasting involves dynamically building and applying models simultaneously to enhance accuracy and reduce model drift.

Tangent offers seven anomaly indicators designed to capture a wide range of anomalies. This ensures that you can detect everything from single-point multivariate deviations to structural patterns that unfold over longer time periods.