# Time Series Completeness — Two-Measure Correlation + Explicit Missing-Value Fill

> Time Series Completeness — Two-Measure Correlation + Explicit 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 Pearson correlation between observed_points and expected_points in a time series and fills missing values using zero, mean, or median imputation — delivered as two independent analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-184?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-two-measure-correlation-explicit-missing-2d1a6ff5
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wWeDtbIcmJBuBrA0o-u7F

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-two-measure-correlation-explicit-missing-2d1a6ff5 -d '<json body>'
```

Example prompt: I have a time series completeness dataset with up to 50 rows — each row has a date, row_id, dataset_group, observed_points, and expected_points. Can you run the two-measure correlation between observed_points and expected_points, fill any missing observed_points values using median imputation, and give me the two analytical reports?

## When to prefer this

Choose this endpoint when you have up to 200 rows of time series completeness records (observed vs expected point counts) and need both a correlation analysis and missing-value imputation in a single call at low cost ($0.01 USDC). It is ideal for offline/batch data quality checks where no live data lookup or prediction is needed. Prefer alternatives if you need outlier flagging (see sibling IQR endpoint), grouped summaries, or more than 200 rows.

## Known failure modes

- Fewer than 1 row supplied — request rejected
- More than 200 rows supplied — exceeds batch limit
- Invalid date format (not YYYY-MM-DD) — validation error
- Both observed_points and expected_points are null for all rows — correlation undefined
- Invalid method enum value — schema validation error
- Invalid field enum value — schema validation error
- Payment not included or incorrect amount — x402 payment required error

## How this service works

Time series completeness — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + 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 independent analytical reports: (1) a Pearson correlation report showing the statistical relationship between observed_points and expected_points across the supplied rows, and (2) a missing-value-filled version of the chosen field (observed_points or expected_points) using the selected imputation method (zero, mean, or median). No predictions, external lookups, or live data — purely local computation on the supplied 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"
  },
  "method": {
   "enum": [
    "zero",
    "mean",
    "median"
   ],
   "type": "string",
   "default": "median"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-two-measure-correlation-explicit-missing-2d1a6ff5/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)