AI-Powered Time Series Forecasting

Submit your historical data via a simple REST API and receive accurate, production-ready forecasts in minutes. No ML expertise required.

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Everything You Need for Accurate Forecasts

Our API handles the complexity so you can focus on decisions, not data science.

Configurable Horizons

Forecast from 7 to 364 days ahead. Choose the exact horizon your business needs.

Multiple Data Intervals

Support for 5-minute, 15-minute, 30-minute, hourly, daily, and weekly intervals.

Event-Based Forecasting

Include holidays, promotions, and custom events so your model understands demand drivers.

Location-Aware Modeling

Provide latitude and longitude to automatically incorporate local weather data into your forecasts.

Timezone Support

Full IANA timezone region support ensures your forecasts align with local business hours.

Exogenous Variables

Pass additional external signals like pricing, marketing spend, or foot traffic to improve accuracy.

Hierarchical Modeling

Organize series by user-defined categories for coherent forecasts across product lines or regions.

Webhook Notifications

Get notified instantly when your forecast is ready. No polling required.

How It Works

Three simple steps from raw data to production forecasts.

1

Submit Historical Data

POST your time series data to the /tasks endpoint with your desired configuration.

2

AI Processes Your Forecast

Our models analyze patterns, seasonality, and external factors. Receive a webhook when complete.

3

Retrieve Results

GET your forecast results with point estimates, ready for integration into your systems.

Simple API Integration

A few lines of Python is all it takes.

forecast.py 
# pip install requests
import requests

API_URL = "https://www.agentforecast.ai/api"
API_KEY = "sk_your_api_key_here"

headers = {"X-API-Key": API_KEY}

# 1. Submit a forecast task
task = requests.post(f"{API_URL}/tasks", headers=headers, json={
    "payload": {
        "parameters": [{"parameter": "forecasting_length", "value": 43200}],
        "data": [{
            "dataset_type": "feature",
            "values": [
                {"datetime": "2025-01-01T00:00:00Z", "value": 142.5},
                # ... more historical data
            ]
        }]
    }
}).json()
task_id = task["data"]["task_id"]

# 2. Check task status
status = requests.get(f"{API_URL}/tasks/{task_id}", headers=headers).json()

# 3. Retrieve results
result = requests.get(f"{API_URL}/tasks/{task_id}/result", headers=headers).json()
for point in result["data"]:
    print(f"{point['datetime']}: {point['value']:.1f}")

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