# Time Series Completeness Analytics — Record Quality Audit, String Normalisation & Grouped Numeric Summary

> Time Series Completeness Analytics — Record Quality Audit, String Normalisation & 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 2026-10-28).

Audits record quality, normalises strings, and produces a grouped numeric summary for time series completeness data (observed vs expected points) across up to 200 rows in three independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-194?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-analytics-record-quality-audit-string-00584908
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_F2vnr7S3xY-AT8I0UGIP8

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-analytics-record-quality-audit-string-00584908 -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and grouped numeric summary on my time series completeness data? I have rows for group-a and group-b across dates like 2026-10-01 through 2026-10-04, with observed_points and expected_points values — analyse the observed_points field.

## When to prefer this

Choose this endpoint when you need simultaneous record quality auditing, string normalisation, and grouped numeric summarisation on time series completeness data (observed vs expected points) in a single $0.01 call covering up to 200 rows. It is ideal for batch data quality checks on pipeline or sensor completeness logs where no external data lookups, predictions, or live enrichment are needed — purely local analytical computation on supplied records.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) causes row rejection
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Payment of 0.01 USDC not included causes 402 payment required response
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Empty rows array returns a minItems validation error

## How this service works

Time series completeness — Record quality audit + String normalisation + Grouped numeric summary: Record quality audit + String normalisation + Grouped numeric summary for supplied time series completeness records (observed_points, expected_points). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.

## Output

Three independent analytical reports returned for the submitted rows: (1) a record quality audit flagging missing, malformed, or invalid entries; (2) a string normalisation report cleaning dataset_group and other string fields; and (3) a grouped numeric summary aggregating observed_points or expected_points statistics by dataset_group and date. All computed locally without 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-analytics-record-quality-audit-string-00584908/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)