# Time Series Completeness – Secondary-Measure Weighted Mean + Missing-Value Fill

> Time Series Completeness – Secondary-Measure Weighted Mean + 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 2026-10-23).

Computes a secondary-measure weighted mean and fills explicit missing values for up to 200 time series completeness records (observed_points, expected_points) using local analytical engines.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-187?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-secondary-measure-weighted-mean-missing-a2e84320
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap__E-HDJd-DTiX0n_szt8i8

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-secondary-measure-weighted-mean-missing-a2e84320 -d '<json body>'
```

Example prompt: I have 4 days of completeness data across two groups — row-1 group-a 2026-10-01 observed 10 expected 1, row-2 group-b 2026-10-02 observed 20 expected 2, row-3 group-a 2026-10-03 observed 15 expected 3, row-4 group-b 2026-10-04 observed 30 expected 4 — can you compute the secondary-measure weighted mean on the observed_points field with median fill for missing values and give me both completeness reports?

## When to prefer this

Choose this endpoint when you need fast, local, purely analytical computation of weighted means and missing-value imputation for time series completeness data (observed vs expected point counts) without requiring any external data lookup, prediction, or live enrichment. Ideal for batch audits of up to 200 rows where you want two distinct reports in one call at minimal cost ($0.01 USDC).

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes row rejection
- Missing required fields (row_id, date, observed_points, expected_points) cause validation errors
- All values null in the target field may produce undefined weighted mean
- Invalid enum for method or field causes schema validation failure

## How this service works

Time series completeness — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + 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 secondary-measure weighted mean computed over the specified field (observed_points or expected_points) across all supplied rows, and (2) explicit missing-value fills for null entries in that field using the chosen method (zero, mean, or median). Reports are produced locally without any external lookup or live data.

## 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-secondary-measure-weighted-mean-missing-a2e84320/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)