# Transformation Throughput Analytics — Trailing Mean Series + Missing-Value Fill

> Transformation Throughput Analytics — Trailing Mean Series + 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 2026-10-28).

Computes a trailing moving-average time series and fills missing values in transformation throughput records (processed_rows, duration_seconds) for up to 200 rows, returning two analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-154?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/transformation-throughput-analytics-trailing-mean-series-missing-8298c8b1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_lOsDfrYjIcQq2Y6qv-u6k

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 transformation-throughput-analytics-trailing-mean-series-missing-8298c8b1 -d '<json body>'
```

Example prompt: Can you run a trailing mean series with a window of 7 and fill any missing processed_rows values using the median, on this set of transformation throughput records I have for the last 30 days across my pipeline groups?

## When to prefer this

Choose this endpoint when you need both rolling-average smoothing and missing-value imputation applied to transformation throughput data (processed_rows or duration_seconds) in a single API call for up to 200 rows. It is ideal for lightweight, local analytical computation on ETL or data pipeline metrics without requiring an external data warehouse or ML platform. Prefer this over general-purpose analytics services when you need a fast, fixed-cost ($0.01 USDC) two-report bundle specifically designed for throughput time series.

## Known failure modes

- Fewer than 1 row submitted — minimum row count not met
- More than 200 rows submitted — exceeds maximum row limit
- Invalid date format (must be YYYY-MM-DD)
- Window size outside allowed range of 2–200
- Missing required fields (row_id, date, processed_rows, duration_seconds)
- Numeric values outside allowed range (±1,000,000,000,000)
- Invalid enum value for field or method parameters

## How this service works

Transformation throughput — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). 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 of the selected field (processed_rows or duration_seconds) over the specified window size, and (2) a missing-value fill report where nulls in the selected field are replaced using the chosen method (zero, mean, or median). Results are computed locally with no external calls.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "processed_rows",
     "duration_seconds"
    ],
    "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
     },
     "processed_rows": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "duration_seconds": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "processed_rows": 10,
     "duration_seconds": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "processed_rows": 20,
     "duration_seconds": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "processed_rows": 15,
     "duration_seconds": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "processed_rows": 30,
     "duration_seconds": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "processed_rows",
    "duration_seconds"
   ],
   "type": "string",
   "default": "processed_rows"
  },
  "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/transformation-throughput-analytics-trailing-mean-series-missing-8298c8b1/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)