# Time Series Completeness Analytics: Grouped Numeric Summary + Date-by-Category Pivot

> Time Series Completeness Analytics: Grouped Numeric Summary + Date-by-Category Pivot 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 (observed vs expected data points) by producing two local analytical reports: a grouped numeric summary and a date-by-category pivot, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-097?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-grouped-numeric-summary-date-by-6ec0f5b7
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_1lHbabDpAKydHup9jZJoP

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-grouped-numeric-summary-date-by-6ec0f5b7 -d '<json body>'
```

Example prompt: I have a batch of time series completeness records — up to 200 rows each with a row_id, dataset_group, date, observed_points, and expected_points — can you run the grouped numeric summary and date-by-category pivot reports on them using the observed_points field?

## When to prefer this

Choose this endpoint when you have tabular time series completeness data (observed vs expected points) that you want to summarize and pivot without any external data lookups, predictions, or real-time processing. It is ideal for offline or batch analytical passes on up to 200 rows where you need both a grouped statistical summary and a date-by-category pivot in a single low-cost call ($0.01 USDC for both reports).

## Known failure modes

- Fewer than 1 row supplied — minimum row count not met
- More than 200 rows supplied — exceeds maxItems limit
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) in any row
- Invalid date format (must be YYYY-MM-DD)
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000)
- Invalid field enum value — must be 'observed_points' or 'expected_points'
- Payment failure — insufficient USDC for x402 transaction

## How this service works

Time series completeness — Grouped numeric summary + Date by category pivot: Grouped numeric summary + Date by category pivot 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 analytical reports computed locally from the supplied rows: (1) a grouped numeric summary with aggregate statistics on observed_points or expected_points broken down by dataset_group, and (2) a date-by-category pivot table showing the selected field values cross-tabulated by date and dataset_group. No external lookups or predictions are made.

## 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-grouped-numeric-summary-date-by-6ec0f5b7/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)