# Time Series Completeness — Numeric Interval Filter + Base-100 Series > Time Series Completeness — Numeric Interval Filter + Base-100 Series 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). Filters time series completeness records by a numeric interval and generates a Base-100 indexed series for up to 200 rows of observed vs. expected data points, producing two independent analytical reports. ## Facts - Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-128?utm_source=zero.xyz - Price: $0.01/call - Payment: x402 - Status: unknown - Last checked: 2026-10-15 - Activations on Zero: 0 - Tags: x402 - Canonical page: https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-base-100-series-b2c764d2 - Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8-ANk88RViP1EhiUNV_EH Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently. ## How to call it through Zero Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`): ```sh zero fetch --capability time-series-completeness-numeric-interval-filter-base-100-series-b2c764d2 -d '' ``` Example prompt: Run a completeness analysis on my time series data — filter the observed_points field to values between 5 and 50, use row index 0 as the base, and here are my 4 rows: row-1 on 2026-10-01 in group-a with 10 observed and 1 expected, row-2 on 2026-10-02 in group-b with 20 observed and 2 expected, row-3 on 2026-10-03 in group-a with 15 observed and 3 expected, row-4 on 2026-10-04 in group-b with 30 observed and 4 expected — give me both the interval-filtered report and the Base-100 series. ## When to prefer this Choose this endpoint when you have up to 200 rows of time series completeness data (observed vs. expected counts) and need both a numeric interval filter and a Base-100 indexed series in a single paid call. It is ideal for offline/local analytical computation where no live data lookup, external enrichment, or prediction is needed — just deterministic statistical transformation of supplied rows. Prefer it over general-purpose analytics when you specifically need the paired interval-filter + base-indexed output in one shot at low cost ($0.01 USDC for both reports). ## Known failure modes - Fewer than 1 or more than 200 rows supplied — request rejected - Date strings not matching YYYY-MM-DD pattern — validation error - observed_points or expected_points outside allowed numeric bounds (±1,000,000,000,000) — validation error - base_index out of range (0–199) or exceeding actual row count — error or unexpected baseline - minimum greater than maximum — may return empty filtered set - Missing required fields (row_id, date, observed_points, expected_points) per row — schema validation failure - Payment of 0.01 USDC not completed via x402 — request blocked ## How this service works Time series completeness — Numeric interval filter + Base-100 series: Numeric interval filter + Base-100 series for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows. ## Output Returns two independent analytical reports: (1) a numeric interval-filtered view of the supplied completeness records showing rows where the selected field (observed_points or expected_points) falls within the specified minimum/maximum range, and (2) a Base-100 indexed series anchored at the specified base_index row, normalizing values relative to that baseline. Both reports are computed locally without external lookups, covering up to 200 input rows. ## Request schema (JSON Schema) ```json { "type": "object", "properties": { "rows": { "type": "array", "items": { "type": "object", "required": [ "row_id", "dataset_group", "date", "observed_points", "expected_points" ], "properties": { "date": { "type": "string", "default": "", "pattern": "^\\d{4}-\\d{2}-\\d{2}$", "maxLength": 10 }, "row_id": { "type": "string", "default": "", "maxLength": 80, "minLength": 1 }, "dataset_group": { "type": "string", "default": "", "maxLength": 80 }, "expected_points": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 }, "observed_points": { "type": [ "number", "null" ], "maximum": 1000000000000, "minimum": -1000000000000 } }, "additionalProperties": false }, "default": [ { "date": "2026-10-01", "row_id": "row-1", "dataset_group": "group-a", "expected_points": 1, "observed_points": 10 }, { "date": "2026-10-02", "row_id": "row-2", "dataset_group": "group-b", "expected_points": 2, "observed_points": 20 }, { "date": "2026-10-03", "row_id": "row-3", "dataset_group": "group-a", "expected_points": 3, "observed_points": 15 }, { "date": "2026-10-04", "row_id": "row-4", "dataset_group": "group-b", "expected_points": 4, "observed_points": 30 } ], "maxItems": 200, "minItems": 1 }, "field": { "enum": [ "observed_points", "expected_points" ], "type": "string", "default": "observed_points" }, "maximum": { "type": "number", "default": 100, "maximum": 1000000000000, "minimum": -1000000000000 }, "minimum": { "type": "number", "default": 0, "maximum": 1000000000000, "minimum": -1000000000000 }, "base_index": { "type": "integer", "default": 0, "maximum": 199, "minimum": 0 } } } ``` ## More - Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-base-100-series-b2c764d2/health.json - [Zero catalog index](https://www.zero.xyz/llms.txt) - [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)