Understanding Features and Entities

In Predicti Data, a feature is a fundamental data model representing a single measurable or descriptive value tracked over time. Features store historical data points, capturing how a specific attribute changes at different timestamps.

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What Is a Feature?

A feature holds one or more values, each associated with a timestamp forming a time series of the attribute's state or measurement.

Feature Structure Example

{
  "history": [
    { "2006-03-08T14:26:56Z": ["2"] },
    { "2007-05-01T10:15:00Z": ["3"] }
  ],
  "id": "11f17ae3-be00-0668-e044-0003ba298018",
  "_lastModified": "2025-06-03T11:21:46.879Z"
}

• history records changes over time: each entry links a timestamp to a list of values (usually a single value per timestamp).
• id uniquely identifies this feature instance.
• _lastModified tracks the last update time of the feature.

This structure enables precise tracking of attribute changes, supporting longitudinal analysis and up-to-date status retrieval.

What Is an Entity?

An entity is a logical grouping or container for related features that collectively describe a real-world object or concept, such as:

• An address
• A person
• A property
• A household

Entities organize features into meaningful sets, making it easier to navigate, query, and manage data.

Example: dk-address Entity Features

• address_houseNumber
• address_postalCode
• address_city
• address_floor
• address_door

Each of these features independently tracks its values over time, maintaining a historical record.

See the DK Address Data Features documentation for more details on the address entity and its features.

Why Use Features with Time-Series History?

Tracking features as time series allows you to:

• Understand how values evolve (e.g., changes in residency type, property size, or postal codes).
• Perform trend analysis and detect patterns over time.
• Access the most current state of an attribute as well as its history for auditing and verification.
• Build dynamic and temporal models that react to real-world changes.

Summary

Concept	Description
Feature	A single attribute storing values with timestamps (time series)
Entity	A collection of related features describing a real-world object

Together, entities and features provide a powerful framework for managing rich, time-aware data that supports accurate and flexible insights.