# Time Series Completeness Analyzer — Numeric Distribution + Keyed Dataset Comparison

> Time Series Completeness Analyzer — Numeric Distribution + Keyed Dataset Comparison 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).

Analyzes time series completeness records (observed_points vs expected_points) by running two independent analytical reports: a numeric distribution analysis and a keyed dataset comparison, for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-084?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-analyzer-numeric-distribution-keyed-dataset-c03115f2
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_fRYry7GWkwfQgv7V6000v

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-analyzer-numeric-distribution-keyed-dataset-c03115f2 -d '<json body>'
```

Example prompt: Analyze the completeness of my time series data — I have up to 50 rows with date, dataset_group, observed_points, and expected_points fields. Run the numeric distribution on observed_points and also compare my primary dataset against a second dataset to spot differences in completeness.

## When to prefer this

Choose this endpoint when you need to audit time series data completeness by comparing observed vs expected data point counts across dates and groups, and want both a statistical distribution view and a direct row-by-row keyed comparison against a reference dataset — all in a single $0.01 call covering up to 200 rows. Prefer this over generic stats tools when your data is already structured as completeness records with group identifiers and you want the two-report combo without setting up your own analytical environment.

## Known failure modes

- Fewer than 1 row or more than 200 rows supplied — request rejected
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) on any row — validation error
- Date field not matching YYYY-MM-DD pattern — schema validation failure
- numeric values outside allowed range (±1,000,000,000,000) — rejected
- Mismatched keys between primary and secondary dataset resulting in no comparable rows — empty comparison report
- Payment of 0.01 USDC not completed via x402 — request not processed

## How this service works

Time series completeness — Numeric distribution + Keyed dataset comparison: Numeric distribution + Keyed dataset comparison 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 derived from the supplied rows: (1) a numeric distribution report showing statistical spread of the selected field (observed_points or expected_points), and (2) a keyed dataset comparison report that matches rows between the primary and secondary (other) dataset by key to surface differences in completeness values. All analysis is performed locally on the supplied data — no external lookups or live data fetching.

## 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}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "expected_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "observed_points": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "dataset_group": "group-a",
     "expected_points": 1,
     "observed_points": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "dataset_group": "group-b",
     "expected_points": 2,
     "observed_points": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "dataset_group": "group-a",
     "expected_points": 3,
     "observed_points": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "dataset_group": "group-b",
     "expected_points": 4,
     "observed_points": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "observed_points",
    "expected_points"
   ],
   "type": "string",
   "default": "observed_points"
  },
  "other": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "observed_points",
     "expected_points"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "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":
… (truncated)
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-analyzer-numeric-distribution-keyed-dataset-c03115f2/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)