expanso-aggregate-time-windows

To aggregate time-series metrics, this Skill applies tumbling and sliding window patterns to calculate averages, min/max values, and data quality scores. It validates raw event data and transforms it into aggregated insights for trend analysis.

Yes, you can use Expanso to calculate average and min/max temperatures from sensor events. The pipeline groups event data into 1-minute tumbling or 5-minute sliding windows, enabling you to identify trends and anomalies in metrics.

The best way to validate input data for time-window aggregation is to run the provided script, which cleans and validates raw event data before processing. This ensures accurate multi-level aggregations and prevents malformed metrics from skewing results.

Yes, the time-series aggregation pipeline outputs aggregated results directly to HTTP endpoints or files. This allows downstream systems to consume the calculated metrics, averages, and data quality scores seamlessly.

You need multi-level aggregation patterns for metrics data when analyzing complex time-series streams that require nested time intervals. This approach processes sensor events hierarchically to capture granular trends and broader anomalies simultaneously.