# Feature Availability Analytics — Trailing Mean Series & Date-by-Category Pivot

> Feature Availability Analytics — Trailing Mean Series & 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 2026-10-20).

Computes a trailing mean time series and a date-by-category pivot table from supplied feature availability records (populated_features, expected_features) for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-152?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-20
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/feature-availability-analytics-trailing-mean-series-date-by-category-76ff03fe
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_HSZsVQHyvaj1B86biAD0I

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 feature-availability-analytics-trailing-mean-series-date-by-category-76ff03fe -d '<json body>'
```

Example prompt: Can you run a trailing mean series and date-by-category pivot on my feature availability data — I have 4 rows across group-a and group-b with dates from 2026-10-01 to 2026-10-04, using populated_features as the field and a window of 2?

## When to prefer this

Choose this endpoint when you have up to 200 rows of feature availability records and need both a rolling mean time series and a date-by-category pivot in a single low-cost call ($0.01 USDC). It is ideal for data quality monitoring, ML feature store auditing, and offline analytics where all data is supplied by the caller and no external lookups or live data sources are needed. Prefer this over general-purpose analytics tools when the specific combination of trailing mean and pivot reports on populated_features/expected_features fields fits your use case.

## Known failure modes

- Fewer than 1 row or more than 200 rows submitted — request rejected
- Window size larger than the number of rows supplied — trailing mean may be empty or partial
- Invalid date format (not YYYY-MM-DD) — row validation failure
- Missing required fields (row_id, date, dataset_group, populated_features, expected_features) — schema validation error
- Null values in populated_features or expected_features may produce NaN entries in mean calculation
- Invalid field enum value (not 'populated_features' or 'expected_features') — request rejected

## How this service works

Feature availability — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot for supplied feature availability records (populated_features, expected_features). 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 trailing mean time series for the selected field (populated_features or expected_features) computed over the specified rolling window, and (2) a date-by-category pivot table breaking down values by date and dataset_group — both derived entirely from the supplied rows, with no external data lookup.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "populated_features",
     "expected_features"
    ],
    "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_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "populated_features": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_features": 1,
     "populated_features": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_features": 2,
     "populated_features": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_features": 3,
     "populated_features": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_features": 4,
     "populated_features": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "populated_features",
    "expected_features"
   ],
   "type": "string",
   "default": "populated_features"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-analytics-trailing-mean-series-date-by-category-76ff03fe/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)