# Data Reconciliation — Trailing Mean Series + Explicit Missing-Value Fill

> Data Reconciliation — Trailing Mean Series + Explicit 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 2026-10-19).

Computes a trailing mean time series and fills missing values in source/target reconciliation data, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-11-154?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-explicit-missing-value-fill-9440c275
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_NskK6on0ZBNb5gdver7mE

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-explicit-missing-value-fill-9440c275 -d '<json body>'
```

Example prompt: I have 50 reconciliation rows with dates, source_total, and target_total values — some source_total entries are null. Can you run a trailing mean series with a window of 5 on the source_total field, filling missing values using median, and give me two analytical reports?

## When to prefer this

Choose this endpoint when you need both a trailing mean time series and missing-value imputation in a single call for reconciliation data with source and target totals. It is ideal for data quality workflows where nulls must be filled before downstream reporting, and where a rolling smoothed series helps identify trends. At $0.01 USDC for two reports covering up to 200 rows, it is well-suited for small-to-medium batch reconciliation jobs that do not require live data lookups, forecasting, or external enrichment.

## Known failure modes

- Fewer than 1 or more than 200 rows submitted — request rejected
- Invalid date format (not YYYY-MM-DD) causes row validation failure
- source_total or target_total values outside ±1,000,000,000,000 range rejected
- Window size less than 2 or greater than 200 causes parameter error
- Missing required fields (row_id, dataset_group, date) cause schema validation failure
- Field enum value not 'source_total' or 'target_total' causes rejection
- Method enum value not 'zero', 'mean', or 'median' causes rejection

## How this service works

Data reconciliation — Trailing mean series + Explicit missing-value fill: Trailing mean series + Explicit missing-value fill 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 analytical reports: a trailing mean time series computed over the selected field (source_total or target_total) with the specified window size, and an explicit missing-value-filled version of the dataset using the chosen method (zero, mean, or median). Both reports are produced locally with no external lookups, covering up to 200 supplied 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"
  },
  "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/data-reconciliation-trailing-mean-series-explicit-missing-value-fill-9440c275/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)