# Time Series Completeness — String Normalisation + Trailing Mean Series

> Time Series Completeness — String Normalisation + Trailing Mean Series is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).

Runs two analytical reports on time series completeness data: string normalisation of dataset group labels and a trailing mean series for observed or expected data points, across up to 200 rows.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-16-028?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-19
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/time-series-completeness-string-normalisation-trailing-mean-series-6a3a0121
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_ccNxQwy8TbIP36bRffJdD

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-trailing-mean-series-6a3a0121 -d '<json body>'
```

Example prompt: Can you run the string normalisation and trailing mean series reports on my time series completeness data? I have 50 rows with observed_points and expected_points per date for two dataset groups, I want the trailing mean over observed_points with a window of 7.

## When to prefer this

Choose this endpoint when you need both string normalisation on dataset group labels and a trailing mean time series computed in a single call over completeness records (observed vs expected data points), without requiring any external data lookup or live prediction. Ideal for offline or batch analytical workflows where you already have the completeness data and want two complementary reports at low cost ($0.01 USDC for up to 200 rows).

## Known failure modes

- Missing required fields (row_id, date, observed_points, expected_points) will cause validation errors
- Date values not matching YYYY-MM-DD pattern will be rejected
- Submitting more than 200 rows exceeds the maxItems limit and will be rejected
- Window size outside the range 2–200 will be rejected
- Null values in the selected field may affect trailing mean calculation accuracy
- Invalid enum value for 'field' parameter (must be 'observed_points' or 'expected_points') will cause an error

## How this service works

Time series completeness — String normalisation + Trailing mean series: String normalisation + Trailing mean 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 analytical reports: (1) a string normalisation report cleaning and standardising the dataset_group label values across the supplied rows, and (2) a trailing mean series computed over the selected field (observed_points or expected_points) using the specified window size, returned for all submitted rows (up to 200).

## 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"
  },
  "window": {
   "type": "integer",
   "default": 2,
   "maximum": 200,
   "minimum": 2
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/time-series-completeness-string-normalisation-trailing-mean-series-6a3a0121/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)