Skip to main content
Skip table of contents

Anomaly Detection Process

Visual Simon build model Doc-6.svg

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.

Category

Subcategory

Supported by Tangent

Anomaly Detection Type

Normal behaviour model with target

Normal Behaviour model without target

Labeling Strategy

Supervised

Semi-Supervised

Unsupervised

Input data type

Continuous data

Categorical data

Input data dimension

Univariate

Multivariate

Temporal context

Continuous processes

Event-driven processes

Batch processes

JavaScript errors detected

Please note, these errors can depend on your browser setup.

If this problem persists, please contact our support.