GETTING STARTED
API REFERENCE
DASHBOARD
EXAMPLES
DOCUMENTATION
Quickstart.
Start with ForecastAPI in 5 minutes. This guide shows you how to make your first forecast request.
PREREQUISITES
You need an API key to start. Sign up for a free account to get your key and 200 free API calls per month.
Get your API key.
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Sign upfor a free account at /register
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Find your API keysNavigate to your Dashboard and click "API Keys"
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Generate a keyClick "Generate New Key" and copy your API key
Prepare your data.
ForecastAPI works with time series data in a simple JSON format. Your data should include:
- identifier: A unique identifier for the data series (e.g., SKU, product ID)
- date: The time period (YYYY-MM-DD) in UTC
- value: The numeric value for that period
Example Data Format
{
"identifier": "SKU-12345",
"data": [
{"date": "2024-01-01", "value": 120},
{"date": "2024-02-27", "value": 135},
{"date": "2024-03-31", "value": 155},
{"date": "2024-04-01", "value": 142},
{"date": "2024-05-01", "value": 168}
],
"periods": 6,
"frequency": "M",
"data_type": "sales"
}
Make your first request.
forecastapi.com
curl -X POST https://forecastapi.com/v2/forecast \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"identifier": "SKU-12345",
"data": [
{"date": "2024-01", "value": 120},
{"date": "2024-02", "value": 135},
{"date": "2024-03", "value": 155}
],
"periods": 6,
"frequency": "M"
}'
# Response in 287ms
{
"forecast": [
{"date": "2024-04", "value": 168.5, "lower": 162.3, "upper": 174.7},
{"date": "2024-05", "value": 175.2, "lower": 168.1, "upper": 182.3},
...
],
"method": "exponential_smoothing",
"confidence": 0.80
}
Understand the response.
ForecastAPI returns your forecast with details on how it produced the forecast:
RESPONSE
{
"result": {
"identifier": "SKU-12345",
"tenant_context": null,
"forecasts": [
{ "period": 1, "date": "2024-06-01", "forecast": 175.2, "lower": 168.1, "upper": 182.3 },
{ "period": 2, "date": "2024-07-01", "forecast": 181.5, "lower": 173.8, "upper": 189.2 }
],
"model_info": {
"best_model": "AutoETS",
"models_evaluated": ["AutoETS", "AutoARIMA", "AutoTheta", "SeasonalNaive"],
"selection_metric": "smape",
"interval_source": "conformal"
}
},
"meta": {
"selection_metric": "smape",
"timing": { "validation": 8.2, "selection": 45.6, "forecasting": 72.1, "total": 125.9 }
}
}
Response Fields
result.forecasts
Array of forecast periods — each has
period, date, forecast, and lower/upper bounds
result.identifier
This echoes the series identifier you sent in the request.
result.model_info
The model that the API automatically selected, the models evaluated, and their back-testing scores
meta
The selection metric used and per-stage timings (in milliseconds)
Common parameters.
Data type
Specify your data type for optimized model selection:
"sales"- Sales data (uses intermittent demand methods for sparse data)"demand"- Demand forecasting"inventory"- Inventory levels"web_traffic"- Website analyticsCustom values- Any string for custom data types
Frequency
Specify your data frequency:
"D"- Daily"W"- Weekly"M"- Monthly
"Q"- Quarterly"Y"- Yearly"H"- Hourly