# HubVibe BigQuery Time Series Forecasting

> HubVibe BigQuery Time Series Forecasting is a paid API for AI agents from hubvibe-io.com, paid per call via x402, $10/call, status unknown (last checked 2026-10-27).

Forecast future values of a BigQuery time series using Google's pretrained TimesFM model (AI.FORECAST) without requiring any model training

## Facts

- Endpoint: POST https://hubvibe-io.com/work/data/forecast?utm_source=zero.xyz
- Price: $10/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-27
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/hubvibe-bigquery-time-series-forecasting-f6974d3c
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_CsQUF0XHE8z_LAzeQnms6

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 hubvibe-bigquery-time-series-forecasting-f6974d3c -d '<json body>'
```

Example prompt: Forecast the next 14 periods of my BigQuery table `myproject.analytics.daily_sales`, using `sale_date` as the timestamp column and `revenue` as the data column.

## When to prefer this

Choose this endpoint when you need to forecast time series data already stored in BigQuery without any model training or ML infrastructure setup. It is ideal for analysts and agents with BigQuery access who want to leverage Google's pretrained TimesFM foundation model via AI.FORECAST. Prefer this over custom ML pipelines when speed and zero-training convenience matter more than fine-tuning. It supports multi-series forecasting in a single call, making it efficient for product, region, or segment-level predictions.

## Known failure modes

- Invalid BigQuery table reference (project.dataset.table format required)
- Table not accessible or permissions denied
- Timestamp or value column name not found in schema
- Scan size exceeds max_scan_gib limit
- Insufficient historical data in the table to generate a reliable forecast
- BigQuery AI.FORECAST quota or region restrictions
- Horizon value too large for available data

## How this service works

Time-series forecasting: forecast any BigQuery table with Google's pretrained TimesFM model (AI.FORECAST), nothing to train. Point it at the table, timestamp column and value column and get forecast rows with bounds for the horizon you set, per series with id_cols. Use it for sales, traffic, demand or metric forecasts. Input: table, timestamp_col, data_col; optional horizon, id_cols.

## Output

Returns forecasted time series values for the requested horizon periods beyond the last observed data point in the BigQuery table. For multi-series forecasts (when id_cols are specified), returns separate predictions per series identifier. Output includes projected data point values aligned to the cadence of the source data.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "table": {
   "type": "string"
  },
  "horizon": {
   "type": "integer"
  },
  "id_cols": {
   "type": "array",
   "items": {
    "type": "string"
   }
  },
  "data_col": {
   "type": "string"
  },
  "language": {
   "type": "string",
   "pattern": "^[A-Za-z]{2,3}(-[A-Za-z0-9]{2,8})*$",
   "maxLength": 35
  },
  "max_scan_gib": {
   "type": "number"
  },
  "timestamp_col": {
   "type": "string"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "type": "json",
 "example": {
  "result": {
   "rows": [
    {
     "state_name": "Texas",
     "forecast_value": "8631745.2",
     "confidence_level": "0.95",
     "forecast_timestamp": "2023-03-24 00:00:00"
    }
   ],
   "table": "bigquery-public-data.covid19_nyt.us_states",
   "columns": [
    "state_name",
    "forecast_timestamp",
    "forecast_value",
    "confidence_level",
    "prediction_interval_lower_bound",
    "prediction_interval_upper_bound",
    "ai_forecast_status"
   ],
   "horizon": 10,
   "data_col": "confirmed_cases",
   "row_count": 10,
   "gib_processed": 0.012,
   "timestamp_col": "date"
  },
  "status": "ok",
  "worker": "data.forecast",
  "price_usd": 10,
  "provenance": {
   "steps": [
    {
     "ms": 640,
     "ok": true,
     "step": "forecast",
     "provider": "bigquery"
    }
   ],
   "attempts": 1,
   "elapsed_ms": 660,
   "providers_used": [
    "bigquery"
   ]
  },
  "receipt_id": "rcpt_9f1c2b3a4d5e6f70",
  "receipt_url": "/work/receipts/rcpt_9f1c2b3a4d5e6f70"
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/hubvibe-bigquery-time-series-forecasting-f6974d3c/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from hubvibe-io.com](https://www.zero.xyz/host/hubvibe-io.com/llms.txt)