# Time Series Completeness: Category Frequencies + Daily Sum Series

> Time Series Completeness: Category Frequencies + Daily Sum 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 by computing category frequencies and a daily sum series for observed vs expected data points across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-102?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-daily-sum-series-1583d95e
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_4htGg-xc0S4GV6SVSBsMw

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-daily-sum-series-1583d95e -d '<json body>'
```

Example prompt: Analyze the completeness of my time series data — I have records for group-a and group-b from 2026-10-01 to 2026-10-04, each with observed_points and expected_points. Give me the category frequencies by dataset_group and the daily sum series for observed_points across up to 4 rows.

## When to prefer this

Choose this endpoint when you need a quick, low-cost dual-report analysis of time series completeness data — specifically category frequency counts and daily aggregated sums — without requiring a live database, external lookup, or prediction engine. It is ideal for batch sizes up to 200 rows where you supply the raw completeness records and want structured analytical summaries returned immediately.

## Known failure modes

- Missing required fields (row_id, date, dataset_group, observed_points, expected_points) cause validation errors
- More than 200 rows submitted exceeds the maxItems limit and will be rejected
- Invalid date format (not YYYY-MM-DD) fails pattern validation
- Null numeric values for both observed_points and expected_points on all rows may produce empty aggregations
- Payment failure via x402 protocol prevents endpoint execution

## How this service works

Time series completeness — Category frequencies + Daily sum series: Category frequencies + Daily sum 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

Two independent analytical reports: (1) a category frequency breakdown showing how often each dataset_group appears in the submitted rows, and (2) a daily sum series aggregating the chosen field (observed_points or expected_points) by date across all rows. 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"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-category-frequencies-daily-sum-series-1583d95e/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)