# Time Series Completeness Audit: Record Quality, String Normalisation & Two-Measure Correlation

> Time Series Completeness Audit: Record Quality, String Normalisation & Two-Measure Correlation 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).

Runs three bundled analytical reports on time series completeness data (observed_points vs expected_points): a record quality audit, string normalisation, and two-measure correlation — all offline, no external lookups.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-204?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-audit-record-quality-string-normalisation-6e416f3d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_xk4EB57sTaf6gpK8R2AYT

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-audit-record-quality-string-normalisation-6e416f3d -d '<json body>'
```

Example prompt: Can you audit the quality of my time series completeness data, normalise any string fields, and check the correlation between observed_points and expected_points for these 10 daily rows across two dataset groups: group-a and group-b?

## When to prefer this

Choose this endpoint when you need three bundled analytical reports (quality audit, string normalisation, correlation) on time series completeness data in a single low-cost call. Ideal for batch offline analysis of up to 200 rows of observed vs expected data point records where no live data lookup, prediction, or external action is required. Best suited when cost efficiency matters and you want all three report types together rather than calling separate specialised services.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Rows missing required fields (row_id, date, observed_points, expected_points) are rejected
- Dates not matching YYYY-MM-DD pattern cause schema validation failure
- Null values in both numeric measures may degrade correlation output
- Empty rows array is rejected per minItems:1 constraint

## How this service works

Time series completeness — Record quality audit + String normalisation + Two-measure correlation: Record quality audit + String normalisation + Two-measure correlation 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: (1) a record quality audit flagging anomalies or issues in the supplied rows, (2) a string normalisation report for text fields like dataset_group, and (3) a two-measure correlation report quantifying the relationship between observed_points and expected_points across the time series. All computed locally on the supplied data — no external lookups or predictions.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-audit-record-quality-string-normalisation-6e416f3d/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)