# Time Series Completeness — Record Quality Audit & Numeric Distribution

> Time Series Completeness — Record Quality Audit & Numeric Distribution 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).

Runs two analytical reports (record quality audit and numeric distribution) on time series completeness data (observed_points vs expected_points) for up to 200 rows, using local analytical engines with no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-004?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-record-quality-audit-numeric-distribution-20fee37f
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_6ecbDpOeGpGQR4KTRnJ46

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-record-quality-audit-numeric-distribution-20fee37f -d '<json body>'
```

Example prompt: Can you run a record quality audit and numeric distribution report on my time series completeness data? I have up to 50 rows with columns row_id, dataset_group, date, observed_points, and expected_points — analyze the observed_points field.

## When to prefer this

Choose this endpoint when you have tabular time series completeness data (observed vs expected point counts) and need both a record quality audit and a numeric distribution profile in a single call for up to 200 rows. It is ideal for data engineers, analysts, or automated pipelines that need lightweight, local statistical profiling without any external lookups or predictions. Prefer it over general-purpose analytics tools when you want a cost-efficient ($0.01 USDC), focused dual-report output specifically scoped to completeness metrics.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) in any row — validation error
- Date strings not matching YYYY-MM-DD pattern — schema validation failure
- Field selector not one of 'observed_points' or 'expected_points' — enum validation error
- All numeric values are null — distribution report may return empty or degenerate results
- Payment not received or insufficient — x402 payment required error

## How this service works

Time series completeness — Record quality audit + Numeric distribution: Record quality audit + Numeric distribution 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

Two structured analytical reports: (1) a record quality audit highlighting issues such as nulls, malformed entries, or anomalies in the completeness records; and (2) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram) for the selected field (observed_points or expected_points). No external data is fetched — all analysis is performed locally on the supplied 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-record-quality-audit-numeric-distribution-20fee37f/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)