# Time Series Completeness — Numeric Interval Filter + Secondary-Measure Weighted Mean

> Time Series Completeness — Numeric Interval Filter + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Filters time series completeness records by a numeric interval on one measure, then computes a secondary-measure weighted mean across the filtered rows — returning two analytical reports for up to 200 supplied rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-132?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-secondary-measure-85885c4a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_g1QkdiCxiHMWOiUtahobQ

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-secondary-measure-85885c4a -d '<json body>'
```

Example prompt: I have 4 completeness records — row-1 (group-a, 2026-10-01, observed=10, expected=1), row-2 (group-b, 2026-10-02, observed=20, expected=2), row-3 (group-a, 2026-10-03, observed=15, expected=3), row-4 (group-b, 2026-10-04, observed=30, expected=4) — filter them where observed_points is between 0 and 20, then give me the weighted mean using expected_points as the secondary measure.

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records (observed vs expected point counts) and need both an interval-filtered subset and a weighted mean computed locally from your supplied data — no external data fetch, prediction, or live lookup required. Ideal for data quality audits, pipeline monitoring summaries, and batch completeness analytics on up to 200 rows at low cost ($0.01 USDC for 2 reports).

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — request rejected
- date values not matching YYYY-MM-DD pattern — validation error
- minimum greater than maximum — undefined or empty filter result
- null values in observed_points or expected_points affecting weighted mean calculation
- row_id missing or exceeding 80 characters — schema validation failure
- field enum value not one of 'observed_points' or 'expected_points' — rejected

## How this service works

Time series completeness — Numeric interval filter + Secondary-measure weighted mean: Numeric interval filter + Secondary-measure weighted mean 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 analytical reports derived from the supplied completeness rows: (1) rows filtered to those where the selected field (observed_points or expected_points) falls within the specified numeric interval [minimum, maximum], and (2) a secondary-measure weighted mean computed across those filtered rows. Reports are generated locally from supplied data with no external lookups.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-secondary-measure-85885c4a/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)