# Time Series Completeness — Numeric Row Ordering + Date by Category Pivot

> Time Series Completeness — Numeric Row Ordering + 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 2026-10-28).

Analyzes time series completeness data by producing two local analytical reports: numeric row ordering and a date-by-category pivot table, for up to 200 rows of observed vs. expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-122?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-numeric-row-ordering-date-by-category-pivot-6571ec59
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_sZpwmMawaCubEqQIvymoI

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-numeric-row-ordering-date-by-category-pivot-6571ec59 -d '<json body>'
```

Example prompt: I have daily time series completeness records for two groups — 'group-a' and 'group-b' — with dates from 2026-10-01 to 2026-10-04 and both observed_points and expected_points values. Can you run the numeric row ordering and date-by-category pivot reports on observed_points for all 4 rows?

## When to prefer this

Choose this endpoint when you have pre-collected time series completeness records (observed vs. expected data points) and need two structured local analytics reports — numeric row ordering and a date-by-category pivot — without requiring any live data lookup, external enrichment, or prediction. Ideal for data quality audits, ETL monitoring, or completeness dashboards where the data is already in hand and you want fast, low-cost ($0.01) dual-report output on up to 200 rows.

## Known failure modes

- Fewer than 1 row or more than 200 rows supplied — request rejected
- Invalid date format (not YYYY-MM-DD) causes validation error
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause schema rejection
- Invalid field enum value (not 'observed_points' or 'expected_points') causes error
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected

## How this service works

Time series completeness — Numeric row ordering + Date by category pivot: Numeric row ordering + 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 on the supplied data: (1) a numeric row ordering of the time series completeness records, and (2) a date-by-category pivot table showing the selected field (observed_points or expected_points) broken down by dataset_group across dates. No external data is fetched or predicted.

## 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-numeric-row-ordering-date-by-category-pivot-6571ec59/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)