# Time Series Completeness Analyzer — Numeric Distribution + Missing-Value Fill

> Time Series Completeness Analyzer — Numeric Distribution + 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).

Computes a numeric distribution report and fills explicit missing values (zero, mean, or median) for up to 200 time series completeness records with observed and expected point counts.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-085?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-analyzer-numeric-distribution-missing-value-d1c5c493
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_5hsbv5Vv2wufRZDUC5Ehv

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-analyzer-numeric-distribution-missing-value-d1c5c493 -d '<json body>'
```

Example prompt: I have up to 200 rows of time series completeness records with observed_points and expected_points per date and dataset group — can you run a numeric distribution analysis and fill any missing observed_points values using the median method?

## When to prefer this

Choose this endpoint when you need both a statistical distribution summary and missing-value imputation for a time series completeness dataset (observed vs expected points) in a single $0.01 call, without requiring any external lookups or live data. It is ideal for batch data quality checks on up to 200 rows where you want offline, reproducible analytics using zero, mean, or median fill strategies.

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — request rejected
- Invalid date format (not YYYY-MM-DD) — validation error
- Unknown fill method enum value — validation error
- All values in chosen field are null — distribution report may be empty or undefined
- row_id missing or exceeds 80 characters — schema validation failure

## How this service works

Time series completeness — Numeric distribution + Explicit missing-value fill: Numeric distribution + 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 analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram buckets) for the chosen field across the supplied rows, and (2) a table of the same rows with explicit missing values in the chosen field replaced by the selected fill strategy (zero, mean, or median). No external data is fetched; all computation is local.

## 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-analyzer-numeric-distribution-missing-value-d1c5c493/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)