# Time Series Completeness — String Normalisation + Daily Sum Series

> Time Series Completeness — String Normalisation + 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 2026-10-23).

Performs string normalisation and daily sum series analysis on time series completeness records (observed_points, expected_points) for up to 200 rows, returning two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-027?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-string-normalisation-daily-sum-series-36ba1baf
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ZqRtGtGHbhQRIy-iM0raS

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-string-normalisation-daily-sum-series-36ba1baf -d '<json body>'
```

Example prompt: I have a batch of up to 50 daily time series completeness records with observed_points and expected_points — can you run string normalisation on the dataset group labels and compute the daily sum series for the observed_points field?

## When to prefer this

Choose this endpoint when you need two complementary analytical views — string normalisation of categorical labels and a daily aggregate sum — on a batch of time series completeness records in a single low-cost call. Ideal for data quality pipelines, ETL monitoring, and dashboard prep where you supply the data and want fast, local computation without external dependencies. Best suited for batches up to 200 rows priced at $0.01 USDC per call.

## Known failure modes

- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, observed_points, expected_points) — validation error
- Invalid date format (must match YYYY-MM-DD pattern) — schema validation failure
- Null numeric values may affect sum calculations
- Invalid field enum value (must be 'observed_points' or 'expected_points') — rejected
- Empty rows array — minimum 1 item required

## How this service works

Time series completeness — String normalisation + Daily sum series: String normalisation + 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

Returns two independent analytical reports: (1) a string-normalised view of the dataset_group labels across all supplied rows, and (2) a daily sum series aggregating either observed_points or expected_points by date — all computed locally without external lookups or predictions, covering up to 200 input rows.

## 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-string-normalisation-daily-sum-series-36ba1baf/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)