# Feature Availability Trailing Mean Series and Missing Value Fill

> Feature Availability Trailing Mean Series and Missing Value Fill 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 moving average series and fills explicit missing values for feature availability records (populated_features / expected_features) over up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-10-154?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-trailing-mean-series-and-missing-value-fill-a56dcc28
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_Pl6EHGgC2nIEJ8WqRYmEr

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-trailing-mean-series-and-missing-value-fill-a56dcc28 -d '<json body>'
```

Example prompt: Compute a trailing mean series with a window of 7 and fill any missing values using median imputation for my populated_features field — here are my 50 daily feature availability records grouped by dataset_group, each with a date, row_id, populated_features, and expected_features.

## When to prefer this

Choose this endpoint when you need both a rolling/trailing mean time series and missing-value imputation on feature availability records in a single low-cost ($0.01) call. Ideal for ML feature stores, data quality pipelines, or analytics dashboards where you supply your own data (up to 200 rows) and want offline analytical computation without any external data lookups or live API calls.

## Known failure modes

- Fewer than 1 or more than 200 rows supplied — returns validation error
- Invalid date format (not YYYY-MM-DD) — rejected at schema validation
- Window size less than 2 or greater than 200 — rejected at schema validation
- populated_features or expected_features values outside ±1,000,000,000,000 range — schema error
- Missing required fields (row_id, date, dataset_group) — schema validation failure
- All values null for selected field — imputation may produce degenerate output

## How this service works

Feature availability — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill 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 analytical reports: (1) a trailing mean series showing the rolling average over the specified window for the chosen field, and (2) an explicit missing-value fill report replacing nulls using the selected method (zero, mean, or median). Both reports cover up to 200 input rows and are computed locally without 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/feature-availability-trailing-mean-series-and-missing-value-fill-a56dcc28/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)