22 - Defining Time Series Data

A time series is a collection of data points that can be measured over time. As with geospatial data, time series information can appear in tabular datasets or on Ontology object types. It can also be combined with geospatial data to create geo-temporal data. Also like geospatial data, some extra configuration is required to ensure analysts can make full use of time series data in Foundry applications.

📚 Recommended Reading (~5 MIN READ)

Begin by reading this overview of time series data in Foundry. Note especially the bulleted list of conditions for using time series data. Then proceed to this documentation outlining the processes for enabling time series analysis capabilities in Foundry. The rest of this tutorial will assume familiarity with these concepts.

In summary:

• A time series is a collection of data points can be measured over time.
• You should consider using Foundry’s time series capabilities when you have temporal data that is large-scale, high-frequency, and/or recorded over long periods of time.
• Time series datasets have at least three columns: series ID, value, and timestamp.
• A time series object type is created in the Ontology Manager application and is backed by a time series metadata dataset.
• Before a time series dataset can be accessed as time series, you or your colleagues must create and run a time series sync on that dataset.
• Quiver Foundry’s core application for time series analytics.