# Feature Availability Analytics: Trailing Mean Series + Adjacent Row Changes

> Feature Availability Analytics: Trailing Mean Series + Adjacent Row Changes 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 trailing mean time-series and adjacent row change reports for feature availability data (populated_features vs expected_features) over up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-146?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-adjacent-row-7f902869
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_G0Qr5U_8Zo7_Sj4yoxv_r

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-adjacent-row-7f902869 -d '<json body>'
```

Example prompt: Analyze my feature availability records — I have 4 rows across group-a and group-b from 2026-10-01 to 2026-10-04 with populated_features and expected_features values — and give me both a trailing mean series (window size 2) and the adjacent row changes for populated_features.

## When to prefer this

Choose this endpoint when you need both a rolling/trailing mean time-series AND adjacent row delta analysis on feature availability records in a single paid call. It is ideal for data quality monitoring, MLOps pipelines, or analytics workflows where you supply structured rows with populated and expected feature counts and want two complementary statistical views without building local analytics logic.

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Window size outside 2–200 range — rejected
- null values in populated_features or expected_features may affect mean/delta calculations
- Unknown field value (not 'populated_features' or 'expected_features') — enum validation error
- Payment failure — 402 response, no report generated

## How this service works

Feature availability — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes 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 reports derived from the supplied feature availability rows: (1) a trailing mean time-series showing windowed averages of the selected field over the specified window, and (2) an adjacent row changes report showing the delta between consecutive rows — all computed locally with no external lookups.

## 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-adjacent-row-7f902869/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)