# Time Series Completeness — Category Frequencies + Numeric Row Ordering

> Time Series Completeness — Category Frequencies + 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 computing category frequencies for dataset groups and numeric row ordering for observed/expected point values, delivering two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-100?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-category-frequencies-numeric-row-ordering-2737c6ec
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_otvez349PRgBNotFWYkZf

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-category-frequencies-numeric-row-ordering-2737c6ec -d '<json body>'
```

Example prompt: I have a batch of daily time series completeness records for two groups — 'group-a' and 'group-b' — with observed_points and expected_points logged per date from 2026-10-01 to 2026-10-04. Can you run category frequency and numeric row ordering reports on those rows using the observed_points field?

## When to prefer this

Choose this endpoint when you have up to 200 time series completeness records (observed_points and expected_points per date and group) and need two quick, self-contained statistical summaries — category frequencies and numeric ordering — without any external data lookup, prediction, or live data integration. It is ideal for data quality audits, pipeline monitoring dashboards, and batch completeness reviews where both group distribution and ranked ordering are needed in a single call at very low cost.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause schema validation failure
- Invalid date format (not YYYY-MM-DD) triggers pattern validation error
- Null values in both observed_points and expected_points across all rows may yield incomplete ordering output
- Payment not included or insufficient USDC balance prevents execution

## How this service works

Time series completeness — Category frequencies + Numeric row ordering: Category frequencies + 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 category frequency table showing how often each dataset_group value appears across the submitted rows, and (2) a numeric row ordering ranking rows by the selected field (observed_points or expected_points). Both reports are computed locally from the supplied 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-category-frequencies-numeric-row-ordering-2737c6ec/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)