`time_weighted_sum`

Generic Description

Sums value multiplied by elapsed duration until the next point. It is documented separately because time construction, sequence ordering, window parameters, and as-of behavior materially change analytical meaning.

Simple example:

WITH [{ts: 1735689600000, value: 10.0, state: 'ok'}, {ts: 1735689660000, value: 13.0, state: 'ok'}, {ts: 1735689840000, value: 9.0, state: 'warn'}, {ts: 1735690200000, value: 16.0, state: 'ok'}] AS readings
RETURN time_weighted_sum(readings, 'value') AS weighted_sum

Consumer-Level Explanation

Use it for exposure-like metrics where magnitude and duration both matter. Prefer this function when the temporal assumption should be visible to the planner, especially for event streams, freshness checks, and sequence features.

More Detailed Explanation

time_weighted_sum works on ordered temporal or sequence-style values inside Cypher. The practical contract is to assemble the sequence deliberately, choose the time or window parameter explicitly, and return named fields so downstream planner tooling does not invent unsupported window syntax.

Advanced Example

This example names the event stream and operational context before applying time_weighted_sum, which helps planner tooling preserve the intended sequence semantics.

WITH 'sensor-1' AS subject_id,
     [{ts: 1735689600000, value: 10.0, state: 'ok'}, {ts: 1735689660000, value: 13.0, state: 'ok'}, {ts: 1735689840000, value: 9.0, state: 'warn'}, {ts: 1735690200000, value: 16.0, state: 'ok'}] AS readings
RETURN subject_id,
       time_weighted_sum(readings, 'value') AS weighted_sum

Real Use Cases

Real Limitations And Tradeoffs