# Time Series Completeness: Category Frequencies + Secondary-Measure Weighted Mean

> Time Series Completeness: Category Frequencies + Secondary-Measure Weighted Mean 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).

Computes category-level frequencies and a secondary-measure weighted mean for up to 200 time series completeness records (observed vs. expected points), returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-109?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-secondary-measure-6ee737a5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_y-SU3EBdrvQTaaTjdvj6P

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-secondary-measure-6ee737a5 -d '<json body>'
```

Example prompt: I have 4 daily completeness records for two dataset groups — group-a had 10 observed vs 1 expected on 2026-10-01 and 15 observed vs 3 expected on 2026-10-03; group-b had 20 observed vs 2 expected on 2026-10-02 and 30 observed vs 4 expected on 2026-10-04 — can you run the category frequencies and weighted mean report on the observed_points field for these?

## When to prefer this

Choose this endpoint when you have up to 200 pre-collected time series completeness records (observed vs. expected point counts) and need both a categorical frequency breakdown by group and a weighted mean of one numeric measure — all in a single low-cost call. Prefer it over general-purpose analytics APIs when your data is already structured with dataset_group, date, observed_points, and expected_points fields and you want two reports for $0.01 USDC without sending data to a heavyweight analytics platform.

## Known failure modes

- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field not matching YYYY-MM-DD format — schema validation failure
- Null values in numeric fields may affect weighted mean calculation
- Invalid field enum value (not 'observed_points' or 'expected_points') — rejected
- Empty rows array — minItems violation

## How this service works

Time series completeness — Category frequencies + Secondary-measure weighted mean: Category frequencies + Secondary-measure weighted mean 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 reports: (1) category frequencies showing how often each dataset_group appears across the submitted rows, and (2) a weighted mean of the chosen primary field (observed_points or expected_points) using the secondary measure as weights — all 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-category-frequencies-secondary-measure-6ee737a5/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)