# Time Series Completeness — String Normalisation + Grouped Numeric Summary

> Time Series Completeness — 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).

Normalises dataset_group strings and produces grouped numeric summary statistics for time series completeness records (observed_points vs expected_points) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-023?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-string-normalisation-grouped-numeric-summary-97eac58c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_LfUcd1Wcc-49q93kjE_hj

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-string-normalisation-grouped-numeric-summary-97eac58c -d '<json body>'
```

Example prompt: I have a set of daily time series records across two groups — 'group-a' and 'group-b' — with dates from 2026-10-01 to 2026-10-04, and each row has observed_points and expected_points values. Can you normalise the group name strings and give me a grouped numeric summary for the observed_points field?

## When to prefer this

Choose this endpoint when you need both string normalisation of group labels and grouped numeric summarisation of time series completeness data (observed vs expected points) in a single cheap call ($0.01 USDC for 2 reports). Ideal for data quality pipelines, completeness audits, and ETL monitoring workflows where you have up to 200 rows and need offline, deterministic analytics without any external lookup or ML inference.

## Known failure modes

- More than 200 rows submitted — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date string not matching YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range — rejection
- Invalid field enum value — error response
- Empty rows array — schema minimum violation

## How this service works

Time series completeness — String normalisation + Grouped numeric summary: String normalisation + 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 reports: (1) a string normalisation report for the dataset_group column, and (2) a grouped numeric summary of the selected field (observed_points or expected_points), including aggregated statistics per group — all computed locally from the supplied rows, 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-string-normalisation-grouped-numeric-summary-97eac58c/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)