Time Series Data Aggregation

Time series data aggregation computes aggregated metrics over historical telemetry of the current entity at fixed time intervals (hourly, daily, weekly, monthly, etc.) and stores the results as new telemetry keys. For example, an energy meter can automatically generate energyHourlySum, powerHourlyAvg, and powerHourlyMax at the end of each hour.

Unlike Related entities aggregation (which aggregates across multiple entities at the latest point), this type operates on historical windows of a single entity’s data — ideal for analytics, reporting, and long-term KPIs.

Use Time series data aggregation when you need to:

• Generate hourly/daily/weekly aggregates (e.g., hourly average temperature)
• Build KPIs from historical data (daily runtime, peak load, total consumption)
• Pre-aggregate metrics to reduce dashboard and API load
• Track near-real-time aggregates using intermediate results (e.g., “current hour average so far”)
• Handle delayed telemetry — the system recalculates affected intervals when late data arrives

1. Open the Data processing ⇾ Calculated fields page.
2. Click the + Add calculated field ⇾ Create new calculated field in the top-right corner.
3. In General, enter a title, specify the entity the field applies to, and select the Time series data aggregation type.
4. Configure Arguments, Metrics, Aggregation interval, and Output.
5. Click Add to save.

This type reads historical telemetry from the current entity only — Latest telemetry is the only available argument type.

Click Add argument and fill in:

Each metric produces one output telemetry key per interval.

Click Add metric and configure:

You can create multiple metrics from the same argument (e.g., tempAvg, tempMin, tempMax).

Defines how often aggregation runs and how time boundaries align.

Aggregation results are stored with the timestamp corresponding to the start of the aggregation interval (e.g., an hourly aggregate for 01:00–02:00 is stored with the 01:00 timestamp).

Advanced interval options:

Apply offset

Apply await timeout

Produce intermediate result

Results are stored only as time series under the configured metric names.

For output strategy options, see Calculated Fields — Output Strategy.

An Energy Meter 1 device publishes power (kW) and energy (kWh) telemetry continuously. Goal: compute hourly KPIs and store them as new time series keys:

• energyHourlySum — total energy consumption per hour
• powerHourlyAvg — average power per hour
• powerHourlyMax — maximum power per hour

1. Apply the Time Series Data Aggregation calculated field to the Energy Meter 1 device profile.
2. Add argument 1:
   • Time series key: energy
   • Argument name: energy
3. Add argument 2:
   • Time series key: power
   • Argument name: power
4. Add metric 1:
   • Metric name: energyHourlySum
   • Aggregation: Sum
   • Argument name: energy
5. Add metric 2:
   • Metric name: powerHourlyAvg
   • Aggregation: Average
   • Argument name: power
6. Add metric 3:
   • Metric name: powerHourlyMax
   • Aggregation: Max
   • Argument name: power
7. Aggregation interval: Hour. Time zone: your reporting time zone.
8. Output type: Time series.
9. Click Add.

Result: At the start of each hour, the device stores:

{
  "powerHourlyAvg": 12.4,
  "powerHourlyMax": 18.9,
  "energyHourlySum": 6.7
}

These pre-aggregated metrics reduce dashboard load and serve as reporting-ready KPIs.