# Time Series Completeness — Exact Row Deduplication + Two-Measure Correlation

> Time Series Completeness — Exact Row Deduplication + Two-Measure Correlation is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-28).

Deduplicates exact rows and computes two-measure correlation for time series completeness records (observed_points vs expected_points) across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-050?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-28
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-exact-row-deduplication-two-measure-9502e999
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_8W7KKDMW302yc8npNMPa0

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-exact-row-deduplication-two-measure-9502e999 -d '<json body>'
```

Example prompt: I have a batch of time series completeness records with row IDs, dataset groups, dates, observed_points, and expected_points — up to 200 rows — can you deduplicate exact rows and also compute the correlation between observed and expected points across those records?

## When to prefer this

Choose this endpoint when you need to simultaneously clean a time series completeness dataset of exact duplicate rows AND measure the linear relationship between observed and expected data points, all in a single low-cost call. It is ideal for datasets up to 200 rows where no live external data, predictions, or professional assessments are needed — just local statistical computation on supplied records.

## Known failure modes

- Exceeding 200 rows causes rejection
- Missing required fields (row_id, date, observed_points, expected_points) causes validation failure
- Malformed date strings not matching YYYY-MM-DD pattern are rejected
- All rows being duplicates may yield an empty deduplicated set
- Null values in observed_points or expected_points may affect correlation computation
- Insufficient variance in either measure may produce undefined or unreliable correlation

## How this service works

Time series completeness — Exact row deduplication + Two-measure correlation: Exact row deduplication + Two-measure correlation 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) an exact row deduplication report identifying and removing fully duplicate rows from the supplied dataset, and (2) a two-measure correlation report quantifying the statistical relationship between observed_points and expected_points across the deduplicated time series records. Both reports are computed locally without external lookups or predictions.

## 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
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-exact-row-deduplication-two-measure-9502e999/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)