# Time Series Completeness Audit & Missing-Value Fill

> Time Series Completeness Audit & 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 2026-10-15).

Audits data quality and fills missing values in time series completeness records (observed_points, expected_points) for up to 200 rows, producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-019?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-audit-missing-value-fill-a8e8ae09
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_urq8whqA6jMEh3f9dMfYv

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 time-series-completeness-audit-missing-value-fill-a8e8ae09 -d '<json body>'
```

Example prompt: Can you run a quality audit and fill in missing observed_points values (using median fill) on my time series completeness records — I have about 50 rows with dates, dataset groups, observed_points, and expected_points that need checking?

## When to prefer this

Choose this endpoint when you need a combined quality audit and missing-value imputation report on time series completeness data (observed vs expected data points) in a single call. Ideal for data engineers or analysts who want batch processing of up to 200 rows with multiple fill strategies (zero, mean, median) and two simultaneous report types. Not suitable for live data lookup, forecasting, or datasets exceeding 200 rows.

## Known failure modes

- More than 200 rows submitted — exceeds batch limit, request will be rejected
- Invalid date format (not YYYY-MM-DD) causes row validation failure
- Missing required fields (row_id, date, observed_points, expected_points) causes schema validation error
- Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) rejected
- Invalid fill method or field enum value causes parameter error
- Payment of 0.01 USDC not provided or fails — request not processed

## How this service works

Time series completeness — Record quality audit + Explicit missing-value fill: Record quality audit + Explicit missing-value fill for supplied time series completeness records (observed_points, expected_points). 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 reports: (1) a record quality audit highlighting anomalies, nulls, and data quality issues in the submitted completeness records, and (2) a missing-value fill report where the specified field (observed_points or expected_points) has nulls replaced using the chosen method (zero, mean, or median). All processing is local/analytical with no external lookups or predictions.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "observed_points",
     "expected_points"
    ],
    "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
     },
     "expected_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observed_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_points": 1,
     "observed_points": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_points": 2,
     "observed_points": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_points": 3,
     "observed_points": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_points": 4,
     "observed_points": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "observed_points",
    "expected_points"
   ],
   "type": "string",
   "default": "observed_points"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-audit-missing-value-fill-a8e8ae09/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)