# Time Series Completeness — Grouped Numeric Summary + Numeric Row Ordering

> Time Series Completeness — Grouped Numeric Summary + Numeric Row Ordering 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 time series completeness records by producing a grouped numeric summary and a numeric row ordering report for up to 200 rows of observed vs. expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-087?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-numeric-row-ordering-c2520553
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ueQhh33JRLbzV4EfW_aYD

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-numeric-row-ordering-c2520553 -d '<json body>'
```

Example prompt: I have a batch of time series completeness records — can you group and summarize the observed_points field by dataset group and also rank the rows numerically? I have 4 rows: row-1 on 2026-10-01 in group-a with 10 observed and 1 expected, row-2 on 2026-10-02 in group-b with 20 observed and 2 expected, row-3 on 2026-10-03 in group-a with 15 observed and 3 expected, row-4 on 2026-10-04 in group-b with 30 observed and 4 expected.

## When to prefer this

Choose this endpoint when you need two specific local analytics — grouped numeric summary and numeric row ordering — applied to time series completeness data (observed vs. expected points) without requiring any external data lookup, prediction, or live data access. It is ideal when you already have the completeness records in hand and want a cost-effective ($0.01 USDC for both reports) statistical breakdown by group and a ranked ordering of rows. Prefer alternatives if you need string normalization, deduplication, category frequency counts, or more than 200 rows.

## Known failure modes

- Fewer than 1 row submitted — returns validation error
- More than 200 rows submitted — request rejected due to row limit
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — validation error
- Non-numeric values for observed_points or expected_points outside allowed range — rejected
- Invalid field enum value (must be 'observed_points' or 'expected_points') — schema error
- Payment not included or insufficient — x402 payment required error

## How this service works

Time series completeness — Grouped numeric summary + Numeric row ordering: Grouped numeric summary + Numeric row ordering 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 the selected field (observed_points or expected_points) by dataset_group with statistics such as count, mean, min, max, and sum; and (2) a numeric row ordering that ranks all input rows by the selected numeric field. Both reports are computed locally from the supplied data, 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-grouped-numeric-summary-numeric-row-ordering-c2520553/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)