# Time Series Completeness Analytics — Quality Audit + String Normalisation + Daily Sum Series

> Time Series Completeness Analytics — Quality Audit + String Normalisation + Daily Sum 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 (record quality audit, string normalisation, and daily sum series) on time series completeness data (observed vs expected points) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-198?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-analytics-quality-audit-string-normalisation-58f0071d
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_-CIuR-ZpdyJdX_y8nZZEd

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-quality-audit-string-normalisation-58f0071d -d '<json body>'
```

Example prompt: Audit these time series completeness records for me — I have up to 50 rows with columns row_id, dataset_group, date, observed_points, and expected_points — and give me all three reports: the record quality audit, the string normalisation, and the daily sum series, focusing on the observed_points field.

## When to prefer this

Choose this endpoint when you need batch analytical reporting on time series completeness records (observed vs expected point counts) and want three coordinated reports — quality audit, string normalisation, and daily sum series — in a single low-cost call. It is ideal for data pipeline monitoring, ETL completeness checks, and dashboard preparation workflows where up to 200 rows need offline analytical processing without any live external lookups or predictions.

## Known failure modes

- Exceeding 200 rows returns a validation error
- Rows missing required fields (row_id, date, observed_points, expected_points) cause schema rejection
- Invalid date format (non-YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside ±1,000,000,000,000 bounds cause schema rejection
- Empty rows array (minItems:1 violated) returns an error
- Malformed JSON body results in a 400-level error

## How this service works

Time series completeness — Record quality audit + String normalisation + Daily sum series: Record quality audit + String normalisation + Daily sum 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

Returns three independent analytical reports generated from the supplied rows: (1) a record quality audit flagging issues in the completeness data, (2) a string normalisation report cleaning and standardising dataset_group and other string fields, and (3) a daily sum series aggregating observed_points or expected_points totals by date. All analysis is performed locally with 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
  },
  "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-quality-audit-string-normalisation-58f0071d/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)