# Time Series Completeness — Base-100 Series + Date by Category Pivot

> Time Series Completeness — Base-100 Series + 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 2026-10-15).

Computes a Base-100 indexed time series and a date-by-category pivot table from supplied time series completeness records (observed vs expected data points), returning two analytical reports in one call.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-167?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-base-100-series-date-by-category-pivot-246b79f8
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_b27KAFZ7Ooc63_TiudIEj

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-base-100-series-date-by-category-pivot-246b79f8 -d '<json body>'
```

Example prompt: I have completeness records for two dataset groups — group-a and group-b — across dates in October 2026 with observed and expected point counts. Can you run a base-100 series analysis indexed from row 0 on the observed_points field, plus a date-by-category pivot, for up to 4 rows of data?

## When to prefer this

Choose this endpoint when you need both a normalized base-100 time series view and a date-by-category pivot for data completeness records in a single low-cost call. It is ideal for data engineering and data quality workflows where you are tracking observed vs expected data point counts across multiple groups over time and want two complementary analytical views without writing custom code. Prefer this over generic analytics tools when your data fits within 200 rows and you want instant structured output for completeness auditing.

## Known failure modes

- Exceeding 200 rows causes rejection — keep input to maxItems:200
- Missing required fields (row_id, dataset_group, date, observed_points, expected_points) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- base_index out of range (0–199) or pointing to a non-existent row index causes computation error
- Null values in observed_points or expected_points may affect base-100 indexing accuracy
- Payment failure (insufficient USDC balance or x402 protocol error) returns 402 response

## How this service works

Time series completeness — Base-100 series + Date by category pivot: Base-100 series + 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 Base-100 indexed time series normalizing the selected field (observed_points or expected_points) relative to the specified base row, and (2) a date-by-category pivot table cross-tabulating the field values by date and dataset_group. Both reports are derived from the supplied rows with no external data or live 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"
  },
  "base_index": {
   "type": "integer",
   "default": 0,
   "maximum": 199,
   "minimum": 0
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-base-100-series-date-by-category-pivot-246b79f8/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)