# Time Series Completeness — Numeric Interval Filter + Date by Category Pivot

> Time Series Completeness — Numeric Interval Filter + 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).

Analyzes time series completeness records (observed vs expected points) using a numeric interval filter and a date-by-category pivot table, delivering two analytical reports for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-133?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-numeric-interval-filter-date-by-category-pivot-57e25c93
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_liD1yTxO8mZWaeKEe-hHl

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-numeric-interval-filter-date-by-category-pivot-57e25c93 -d '<json body>'
```

Example prompt: I have 50 time series completeness records with observed_points and expected_points per dataset group and date — can you run a numeric interval filter keeping rows where observed_points is between 0 and 100, and also generate a date-by-category pivot, using my supplied data?

## When to prefer this

Choose this endpoint when you need two complementary views of time series completeness data in a single paid call: a numeric range filter to isolate rows within a specific observed/expected point band, plus a date-by-category pivot to visualize group coverage over time. It is ideal for data quality audits, pipeline monitoring, and ingestion QA where you already have the completeness records and need fast local analytics without any external data lookup.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- minimum greater than maximum causing empty interval filter result
- Payment not confirmed (x402 protocol) — 402 response before processing

## How this service works

Time series completeness — Numeric interval filter + Date by category pivot: Numeric interval filter + 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) a numeric interval-filtered subset of the input rows where the selected field (observed_points or expected_points) falls within the specified minimum/maximum range, and (2) a date-by-category pivot table aggregating completeness metrics across dataset groups and dates. Both reports are computed locally from 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
  },
  "field": {
   "enum": [
    "observed_points",
    "expected_points"
   ],
   "type": "string",
   "default": "observed_points"
  },
  "maximum": {
   "type": "number",
   "default": 100,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  },
  "minimum": {
   "type": "number",
   "default": 0,
   "maximum": 1000000000000,
   "minimum": -1000000000000
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-numeric-interval-filter-date-by-category-pivot-57e25c93/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)