# Time Series Completeness — Grouped Numeric Summary + Two-Measure Correlation

> Time Series Completeness — Grouped Numeric Summary + 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 2026-10-15).

Analyzes supplied time series completeness records (observed_points, expected_points) by producing a grouped numeric summary and a two-measure correlation report for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-095?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-grouped-numeric-summary-two-measure-e1dc87b5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_g8A0ZrabU5G2irHkEHarL

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-grouped-numeric-summary-two-measure-e1dc87b5 -d '<json body>'
```

Example prompt: I have up to 200 rows of time series completeness records — each with a date, dataset group, observed_points, and expected_points — can you run a grouped numeric summary and a two-measure correlation report on them?

## When to prefer this

Choose this endpoint when you have up to 200 rows of time series completeness data (observed vs expected point counts) and need both grouped descriptive statistics and a two-measure correlation in a single low-cost call ($0.01 USDC). It is ideal for lightweight, self-contained statistical analysis on supplied data without needing live lookups, predictions, or external integrations.

## Known failure modes

- Input rows array is empty or missing — returns validation error
- More than 200 rows submitted — rejected by maxItems constraint
- Row missing required fields (row_id, dataset_group, date, observed_points, expected_points) — row-level validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric fields outside allowed range (-1000000000000 to 1000000000000) — validation error
- All observed_points or expected_points are null — correlation computation may be undefined or return NaN

## How this service works

Time series completeness — Grouped numeric summary + Two-measure correlation: Grouped numeric summary + Two-measure correlation 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 independent analytical reports: (1) a grouped numeric summary breaking down observed_points or expected_points statistics by dataset_group, and (2) a two-measure correlation between observed_points and expected_points across the supplied rows. Both reports are computed locally on the submitted 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-grouped-numeric-summary-two-measure-e1dc87b5/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)