# Data Freshness Analytics: Trailing Mean Series + Date-by-Category Pivot

> Data Freshness 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 2026-10-22).

Computes a trailing moving-mean time series and a date-by-category pivot table for supplied data freshness records (age_minutes, record_count) — two independent analytical reports in one call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-06-152?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-22
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-freshness-analytics-trailing-mean-series-date-by-category-pivot-c889ca60
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_relprNC5g3XDqsSo-hicc

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-date-by-category-pivot-c889ca60 -d '<json body>'
```

Example prompt: Run a trailing mean series and date-by-category pivot on my data freshness records — the field is age_minutes with a window of 3 — using these rows: group-a on 2026-10-01 had age 10 min and 1 record, group-b on 2026-10-02 had age 20 min and 2 records, group-a on 2026-10-03 had age 15 min and 3 records.

## When to prefer this

Choose this endpoint when you have up to 200 data freshness records with age_minutes and/or record_count values and need both a rolling/trailing mean time series AND a date-by-category pivot in a single low-cost call ($0.01 USDC). Ideal for data engineers or analysts monitoring pipeline freshness across multiple dataset groups over time. Not appropriate if you need live data lookup, forecasting, external enrichment, or more than 200 rows processed.

## Known failure modes

- Fewer rows than the window size yields incomplete or no trailing mean values
- Missing required fields (row_id, dataset_group, date, age_minutes, record_count) cause validation errors
- Date strings not matching YYYY-MM-DD pattern are rejected
- More than 200 rows submitted exceeds the row limit and is rejected
- Null numeric values in the selected field may produce NaN or skipped entries in the mean series
- Invalid field enum value (not age_minutes or record_count) causes a schema error

## How this service works

Data freshness — Trailing mean series + Date by category pivot: Trailing mean series + Date by category pivot 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 moving-mean time series over the chosen field (age_minutes or record_count) using the specified window size, and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group — both computed locally from the supplied rows with no external data access.

## 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-date-by-category-pivot-c889ca60/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)