# Data Reconciliation: Trailing Mean Series + Two-Measure Correlation

> Data Reconciliation: Trailing Mean Series + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Computes a trailing mean time series and a two-measure correlation analysis on supplied source_total/target_total reconciliation data (up to 200 rows, no external lookups).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-150?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/data-reconciliation-trailing-mean-series-two-measure-correlation-1fef3cc1
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_MKfV_6ljKG06fXwXeHAih

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-reconciliation-trailing-mean-series-two-measure-correlation-1fef3cc1 -d '<json body>'
```

Example prompt: Run a trailing mean series and two-measure correlation on my reconciliation data — I have 50 rows with source_total and target_total values dated from 2025-01-01 to 2025-02-19, use a window size of 7 and compute the trailing mean on the source_total field.

## When to prefer this

Choose this endpoint when you need both a rolling/trailing mean time series and a pairwise correlation between two numeric measures (source_total and target_total) in a single paid call on your own supplied data. It is ideal for data reconciliation workflows where you want to smooth temporal noise and quantify agreement between two data sources without any external lookups or predictions. Prefer this over general-purpose analytics tools when you need lightweight, on-demand statistical reconciliation analysis at low cost ($0.01 USDC for both reports).

## Known failure modes

- Fewer than 1 row submitted — minimum item requirement not met
- Window size larger than number of rows — trailing mean cannot be computed
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- source_total or target_total outside allowed range (-1T to +1T) — validation error
- More than 200 rows submitted — exceeds maximum item limit
- Missing required fields (row_id, dataset_group, date) — request rejected

## How this service works

Data reconciliation — Trailing mean series + Two-measure correlation: Trailing mean series + Two-measure correlation for supplied data reconciliation records (source_total, target_total). 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 time series computed over the specified window size for the chosen field (source_total or target_total), and (2) a two-measure correlation report showing the statistical relationship between source_total and target_total across the supplied rows. No external data is fetched; all computation is performed locally on the submitted rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "source_total",
     "target_total"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_total": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "target_total": {
      "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",
     "source_total": 10,
     "target_total": 1,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_total": 20,
     "target_total": 2,
     "dataset_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_total": 15,
     "target_total": 3,
     "dataset_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_total": 30,
     "target_total": 4,
     "dataset_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_total",
    "target_total"
   ],
   "type": "string",
   "default": "source_total"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/data-reconciliation-trailing-mean-series-two-measure-correlation-1fef3cc1/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)