# Time Series Completeness — Exact Row Deduplication + Date by Category Pivot

> Time Series Completeness — Exact Row Deduplication + 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 2026-10-28).

Deduplicates exact rows and generates a date-by-category pivot table from time series completeness records containing observed and expected data points.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-052?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-date-by-category-pivot-7df34c27
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_dFpqj2jx0ALjqYpUa7cEo

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-date-by-category-pivot-7df34c27 -d '<json body>'
```

Example prompt: Can you deduplicate and pivot this time series completeness data? I have up to 50 rows with observed_points and expected_points per dataset_group and date — run both the exact row deduplication and the date-by-category pivot on the observed_points field.

## When to prefer this

Choose this endpoint when you need both exact row deduplication and a date-by-category pivot on time series completeness data in a single low-cost call. It is ideal for data quality workflows where you need to clean duplicates and summarize observed vs expected point counts across groups and dates, without requiring live lookups, predictions, or external data sources. Best suited for datasets of up to 200 rows processed offline.

## Known failure modes

- Input exceeds 200 rows — request rejected with validation error
- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) cause row-level errors
- Invalid date format (not YYYY-MM-DD) triggers schema validation failure
- Null values for observed_points or expected_points may affect pivot aggregation
- Field enum value other than 'observed_points' or 'expected_points' causes validation error

## How this service works

Time series completeness — Exact row deduplication + Date by category pivot: Exact row deduplication + 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) an exact row deduplication report identifying and removing duplicate records from the supplied rows, and (2) a date-by-category pivot table showing the selected metric (observed_points or expected_points) broken down by date and dataset_group. Up to 200 rows are processed per call.

## 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-exact-row-deduplication-date-by-category-pivot-7df34c27/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)