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Time Series Completeness — Numeric Interval Filter + Adjacent Row Changes
x402
Time Series Completeness — Numeric Interval Filter + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Analyzes time series completeness records by applying a numeric interval filter and detecting adjacent row changes across observed_points and expected_points fields, returning two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Time series completeness — Numeric interval filter + Adjacent row changes: Numeric interval filter + Adjacent row changes 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 numeric interval filter report identifying which rows have the selected field (observed_points or expected_points) within the specified minimum/maximum bounds, and (2) an adjacent row changes report showing differences between consecutive rows for the same field, for up to 200 input rows.
Example use cases
Daily completeness gap detection
“I have 120 rows of daily time series completeness records across two dataset groups. Can you filter rows where expected_points falls between 5 and 500, and also flag any adjacent rows that show a big change in the expected_points values?”
Audit observed data point spikes
“Run an interval filter on my 80-row completeness dataset — I want to see which rows have observed_points between 10 and 250 — and also give me a report on adjacent row changes in observed_points so I can spot sudden spikes or drops.”
Multi-group time series QA
“I've got completeness records for several dataset groups going back 6 months, about 175 rows total. Can you apply a numeric filter to keep only rows where observed_points is between 0 and 1000, and at the same time tell me where consecutive rows change significantly?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-16-127?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to simultaneously (1) filter time series completeness records by a numeric range on observed or expected points, and (2) detect value changes between adjacent rows — all in a single paid call at $0.01 USDC for up to 200 rows. Prefer this over single-report endpoints when both interval filtering and row-to-row change analysis are needed together without external lookups or live data sources.
Side effects
- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- Invalid date format (must match YYYY-MM-DD pattern)
- Minimum value greater than maximum value resulting in an empty interval
- Null values in observed_points or expected_points may affect change detection calculations
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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