# Time Series Completeness — Record Quality Audit + String Normalisation + Base-100 Series

> Time Series Completeness — Record Quality Audit + String Normalisation + Base-100 Series 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 three local analytical reports on time series completeness data: a record quality audit, string normalisation, and a base-100 indexed series — for up to 200 rows of observed vs expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-201?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-string-normalisation-1c0b594c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_u-z3PcyI5wD6Vudh6C4jP

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-string-normalisation-1c0b594c -d '<json body>'
```

Example prompt: Can you run a record quality audit, string normalisation, and base-100 series (indexed from row 0) on my time series completeness data? I have daily records for group-a and group-b with observed_points and expected_points from 2026-10-01 to 2026-10-04 — use observed_points as the series field.

## When to prefer this

Choose this endpoint when you need three bundled analytical reports — quality audit, string normalisation, and base-100 indexing — on time series completeness data (observed vs expected points) in a single cost-efficient call. Ideal for data pipeline monitoring, completeness dashboards, or batch QA workflows where you want local computation with no external dependencies. Prefer it when you have up to 200 rows and want all three report types without making separate API calls.

## Known failure modes

- Rows exceed 200-item limit — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not in YYYY-MM-DD format — schema validation failure
- base_index out of range (0–199) — error returned
- Null values in numeric fields may affect series computation
- field enum value not 'observed_points' or 'expected_points' — schema rejection

## How this service works

Time series completeness — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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 analytical reports returned together: (1) a record quality audit flagging issues in the input rows, (2) a string normalisation report standardising text fields like dataset_group, and (3) a base-100 series re-indexing the chosen numeric field relative to a specified baseline row. All computed locally from 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-record-quality-audit-string-normalisation-1c0b594c/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)