# Time Series Completeness — Category Frequencies + Base-100 Series

> Time Series Completeness — Category Frequencies + Base-100 Series 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 to produce category frequency distributions and a base-100 indexed series report for up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-105?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-category-frequencies-base-100-series-46f43f4b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_cWu0eqRgmMGkwH2Kc2sDX

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-category-frequencies-base-100-series-46f43f4b -d '<json body>'
```

Example prompt: Can you run two analytics reports on my time series completeness data — category frequency breakdown and a base-100 indexed series — using the observed_points field, starting the base index at row 0? Here are my records: group-a had 10 observed vs 1 expected on 2026-10-01, and group-b had 20 observed vs 2 expected on 2026-10-02.

## When to prefer this

Choose this endpoint when you need both a categorical frequency summary and a relative base-100 trend index on time series completeness data in a single paid call. It is ideal for data quality audits comparing observed vs expected coverage across multiple groups over time, without requiring any external data lookups or live connections. Prefer it over generic stats tools when your data fits the row schema (date + group + observed/expected points) and you want two distinct analytical views from one API call at low cost.

## Known failure modes

- Submitting more than 200 rows returns a validation error
- Missing required fields (row_id, date, observed_points, expected_points) cause row-level rejection
- Invalid date format (not YYYY-MM-DD) triggers a pattern validation error
- base_index out of range (>= number of submitted rows) causes an indexing error
- Null values in observed_points or expected_points may affect series calculation
- Payment failure (insufficient USDC balance) blocks the request

## How this service works

Time series completeness — Category frequencies + Base-100 series: Category frequencies + Base-100 series 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 category frequency table showing how often each dataset_group appears in the submitted rows, and (2) a base-100 indexed series for the selected field (observed_points or expected_points) anchored at the specified base_index row, enabling relative trend comparison across time.

## 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-category-frequencies-base-100-series-46f43f4b/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)