# Time Series Completeness — Grouped Numeric Summary + Missing-Value Fill

> Time Series Completeness — Grouped Numeric Summary + 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 grouped numeric summaries and fills explicit missing values for time series completeness records (observed_points vs expected_points) across up to 200 rows in two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-099?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-grouped-numeric-summary-missing-value-fill-017e4262
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_G__TSX14tTuYpSgYoyz9V

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-grouped-numeric-summary-missing-value-fill-017e4262 -d '<json body>'
```

Example prompt: I have up to 50 rows of time series completeness data with observed_points and expected_points grouped by dataset — can you run grouped numeric summaries and fill any missing observed_points values using median fill, then give me two reports?

## When to prefer this

Choose this endpoint when you need quick grouped statistical summaries and missing-value imputation on time series completeness records (observed vs expected data points) without any external data lookup, live API calls, or predictions. It is ideal for batch data quality audits where you supply the rows directly and want two reports (summary + filled series) in a single call for a fixed low cost. Prefer alternatives if you need forecasting, anomaly detection, or more than 200 rows.

## Known failure modes

- Input array is empty or exceeds 200 rows — validation error
- Missing required fields (row_id, date, observed_points, expected_points) on any row — schema error
- Date strings not in YYYY-MM-DD format — pattern validation failure
- Field or method enum value not recognized — enum validation error
- All values for the target field are null — fill may produce degenerate results
- Payment not included or insufficient — x402 payment required error

## How this service works

Time series completeness — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + 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 analytical reports: (1) a grouped numeric summary (e.g. count, mean, min, max, std) of the observed_points and/or expected_points fields segmented by dataset_group, and (2) an explicit missing-value-filled version of the chosen field using the specified fill method (zero, mean, or median). Both reports are based solely on the supplied rows (up to 200).

## 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-grouped-numeric-summary-missing-value-fill-017e4262/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)