# Time Series Completeness — Record Quality Audit & Grouped Numeric Summary

> Time Series Completeness — Record Quality Audit & Grouped Numeric Summary 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 on supplied time series completeness data (observed vs expected points): a record quality audit and a grouped numeric summary, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-005?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-grouped-numeric-summary-736c0f2a
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Q6OBU3Wh6Rs4Pr6VbfxKS

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-grouped-numeric-summary-736c0f2a -d '<json body>'
```

Example prompt: Can you audit the quality and give me a grouped numeric summary for my time series completeness data? I have rows with dataset groups like 'sensors-east' and 'sensors-west', dates from 2025-01-01 to 2025-01-04, and observed_points vs expected_points values — I want the observed_points field summarized by group.

## When to prefer this

Choose this endpoint when you need simultaneous record-level quality auditing AND grouped statistical summaries of time series completeness data in a single API call. It is ideal when you have structured daily records with observed and expected point counts across multiple named dataset groups and want both data validation and descriptive analytics without any external data fetching or prediction. At $0.01 USDC for both reports on up to 200 rows, it is cost-effective for batch completeness review tasks.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause schema rejection
- Invalid date format (non-YYYY-MM-DD) triggers a pattern validation error
- Supplying zero rows results in a minItems violation
- Numeric values outside the allowed range (-1,000,000,000,000 to 1,000,000,000,000) are rejected
- Invalid field enum value (anything other than 'observed_points' or 'expected_points') causes a request error

## How this service works

Time series completeness — Record quality audit + Grouped numeric summary: Record quality audit + Grouped numeric summary 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 record quality audit that flags issues such as missing values, nulls, or anomalies in the supplied rows, and (2) a grouped numeric summary that aggregates the chosen field (observed_points or expected_points) by dataset_group, providing statistics like count, mean, min, max, and totals per group. Both reports are computed locally on the 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-record-quality-audit-grouped-numeric-summary-736c0f2a/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)