# Time Series Completeness — Exact Row Deduplication + Category Frequencies

> Time Series Completeness — Exact Row Deduplication + Category Frequencies is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).

Analyzes time series completeness records (observed vs expected data points) by deduplicating exact duplicate rows and computing category frequencies across dataset groups, returning two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-041?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-24
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-exact-row-deduplication-category-frequencies-9b40c5cf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_CBjqCGq15W4af5vPFfXdk

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-category-frequencies-9b40c5cf -d '<json body>'
```

Example prompt: Can you deduplicate and compute category frequencies for my time series completeness records? I have rows with row_id, dataset_group, date, observed_points, and expected_points — here are my entries: row-1 for group-a on 2026-10-01 with 10 observed and 1 expected, row-2 for group-b on 2026-10-02 with 20 observed and 2 expected, and row-1 for group-a on 2026-10-01 with 10 observed and 1 expected (a duplicate).

## When to prefer this

Choose this endpoint when you need two specific analyses on time series completeness data in a single call: exact duplicate row detection/removal and category frequency distribution by dataset group. Ideal for data quality audits, ETL validation pipelines, or completeness reporting workflows where you have up to 200 rows of observed vs expected point counts keyed by date and group. Prefer this over general-purpose analytics tools when you want a lightweight, paid-per-call micro-service that combines both deduplication and frequency reporting without requiring infrastructure setup.

## Known failure modes

- Fewer than 1 row submitted — validation error
- More than 200 rows submitted — rejected, exceeds max item limit
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) — schema validation failure
- Malformed date format (not YYYY-MM-DD) — pattern validation error
- observed_points or expected_points outside allowed numeric range — validation error
- Payment not provided or insufficient — 402 payment required

## How this service works

Time series completeness — Exact row deduplication + Category frequencies: Exact row deduplication + Category frequencies 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 independent analytical reports: (1) an exact row deduplication report identifying and removing fully duplicate rows from the submitted completeness records, and (2) a category frequency report showing how often each dataset_group value appears across the submitted rows — all computed locally against the supplied data with no external lookups.

## 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-category-frequencies-9b40c5cf/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)