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Time Series Completeness — Trailing Mean + Two-Measure Correlation
x402
Time Series Completeness — Trailing Mean + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-15).
Computes a trailing mean series and a two-measure Pearson correlation for time series completeness records (observed_points vs expected_points) across up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Returns two independent analytical reports: (1) a trailing mean series smoothed over the specified window for the chosen field (observed_points or expected_points), and (2) a Pearson correlation coefficient between observed_points and expected_points across the supplied rows. Both reports are derived locally from the submitted data with no external lookup.
Example use cases
Monitor pipeline data completeness trends
“I have daily completeness records for my ETL pipeline — observed_points versus expected_points for each dataset group — can you compute a 14-day trailing mean and the correlation between observed and expected across 90 rows?”
QA reporting on sensor data gaps
“We're auditing sensor data collection: I have 150 rows with observed and expected point counts per day across multiple sensor groups. Run a trailing mean with window 5 on observed_points and also give me the two-measure correlation between observed and expected.”
Benchmark two metrics in completeness time series
“I want to understand how closely our observed data points track our expected targets. Please compute a trailing mean series using a window of 10 and the correlation between observed_points and expected_points for my 60-row completeness dataset.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-150?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a smoothed trailing mean series and a correlation analysis on time series completeness data (observed vs expected points) in a single call. It is ideal for data quality monitoring workflows where you want to quantify data collection gaps and measure how closely observed volumes track expected volumes over time, without needing external data enrichment or live predictions.
Side effects
- Fewer than 1 row supplied — validation error
- More than 200 rows submitted — exceeds maximum and will be rejected
- Window size larger than the number of rows — insufficient data for trailing mean
- Missing required fields (row_id, date, observed_points, expected_points) — schema validation failure
- Malformed date strings (not YYYY-MM-DD) — pattern validation error
- All null values in a measure — correlation computation undefined
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| window | integer |
Tags
x402
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