# Data Freshness Analytics — Trailing Mean Series + Adjacent Row Changes

> Data Freshness 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 2026-10-18).

Computes two analytical reports on data freshness records: a trailing moving-average series and adjacent row-to-row delta changes, for up to 200 rows of age_minutes or record_count data.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-146?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-18
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-analytics-trailing-mean-series-adjacent-row-changes-11a6819c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_16RXQqhNt4VpB14hC--Wg

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 data-freshness-analytics-trailing-mean-series-adjacent-row-changes-11a6819c -d '<json body>'
```

Example prompt: I have 50 rows of data freshness records with date, age_minutes, and record_count — can you run both a trailing mean series and adjacent row change report on the age_minutes field using a window size of 5?

## When to prefer this

Choose this endpoint when you have up to 200 rows of data freshness records (age_minutes, record_count) and need both a trailing moving-average time series and adjacent row-to-row delta analysis in a single call. It is ideal for lightweight, offline analytical summaries of freshness metrics without requiring a full data warehouse or streaming pipeline. Prefer it over general-purpose analytics tools when cost efficiency ($0.01 for two reports) and deterministic local computation are priorities.

## Known failure modes

- Fewer rows than window size causes incomplete or no trailing mean values at the start of the series
- Missing or null age_minutes/record_count values may produce null deltas or means for affected rows
- Rows exceeding the 200-row limit will be rejected
- Invalid date format (not YYYY-MM-DD) causes validation error
- Empty rows array returns an error

## How this service works

Data freshness — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes for supplied data freshness records (age_minutes, record_count). 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 series showing the rolling average of the selected field (age_minutes or record_count) over the specified window, and (2) adjacent row changes showing the difference between each consecutive row's values. Both reports are 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",
     "age_minutes",
     "record_count"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "age_minutes": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "record_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "age_minutes": 10,
     "record_count": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "age_minutes": 20,
     "record_count": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "age_minutes": 15,
     "record_count": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "age_minutes": 30,
     "record_count": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "age_minutes",
    "record_count"
   ],
   "type": "string",
   "default": "age_minutes"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-freshness-analytics-trailing-mean-series-adjacent-row-changes-11a6819c/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)