# Time Series Completeness: Weighted Mean + Date-by-Category Pivot

> Time Series Completeness: Weighted Mean + Date-by-Category Pivot 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 secondary-measure weighted mean and a date-by-category pivot table for up to 200 time series completeness records (observed_points vs expected_points) in a single call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-185?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-weighted-mean-date-by-category-pivot-dc703ce0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_NM0v5n2M3eD69u-Ov9VEH

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-weighted-mean-date-by-category-pivot-dc703ce0 -d '<json body>'
```

Example prompt: I have completeness records for two dataset groups over 4 days — group-a and group-b — with observed_points and expected_points for each day. Can you run a weighted mean and a date-by-category pivot on the observed_points field for those rows?

## When to prefer this

Choose this endpoint when you need both a weighted mean aggregation and a date-by-category pivot on time series completeness data (observed vs expected points) in a single cheap call ($0.01 USDC). Ideal for lightweight, local analytics on up to 200 rows without needing external data sources, predictions, or ML models. Best suited for data quality monitoring, pipeline completeness dashboards, and coverage reporting scenarios where you control the input data.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) — validation error
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null values in observed_points or expected_points may affect weighted mean calculation
- Invalid field enum value (not 'observed_points' or 'expected_points') — rejected

## How this service works

Time series completeness — Secondary-measure weighted mean + Date by category pivot: Secondary-measure weighted mean + Date by category pivot 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 secondary-measure weighted mean of the selected field (observed_points or expected_points) across the supplied rows, and (2) a date-by-category pivot table cross-tabulating values by date and dataset_group — both computed locally without external lookups, covering up to 200 input rows.

## 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-weighted-mean-date-by-category-pivot-dc703ce0/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)