# Time Series Completeness — Date×Category Pivot + Missing-Value Fill

> Time Series Completeness — Date×Category Pivot + 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).

Pivots time series completeness records (observed vs expected data points) by date and category, then fills missing values using zero, mean, or median imputation — delivering two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-189?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-date-category-pivot-missing-value-fill-fbbcaac4
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_UKgd8N25xFyzH-IvE3fH9

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-date-category-pivot-missing-value-fill-fbbcaac4 -d '<json body>'
```

Example prompt: I have a completeness dataset with up to 150 rows — each row has a date, a dataset_group, and observed_points plus expected_points values. Can you pivot it by date and category and fill any missing observed_points using median imputation?

## When to prefer this

Choose this endpoint when you have tabular time series completeness records with observed and expected point counts per date and category, and you need both a date×category pivot view and missing-value imputation in a single paid call. Prefer it over generic spreadsheet tools when you want a structured API response for up to 200 rows at low cost ($0.01 USDC for two reports). Not suitable for live data lookups, forecasting, or datasets exceeding 200 rows.

## Known failure modes

- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) cause validation errors
- More than 200 rows submitted — request rejected with row-count error
- Invalid date format (not YYYY-MM-DD) causes parse failure
- Invalid enum value for 'field' or 'method' returns schema validation error
- Empty rows array returns an error
- All values missing for a group may produce degenerate imputation results

## How this service works

Time series completeness — Date by category pivot + Explicit missing-value fill: Date by category pivot + 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

Two analytical reports: (1) a date-by-category pivot table of the specified field (observed_points or expected_points) across all supplied rows, and (2) the same data with explicit missing values filled using the chosen method (zero, mean, or median). Both reports are computed locally from the supplied data with no external lookups.

## 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-date-category-pivot-missing-value-fill-fbbcaac4/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)