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Quick start

Install skforecast-ai and verify the setup works. For a step-by-step walkthrough of your first real forecast, continue to Your first forecast.

New to forecasting with machine learning? skforecast's Introduction to forecasting covers the fundamentals this documentation assumes.

Install

Core library — no API key needed, runs entirely offline:

pip install skforecast-ai

Adds the optional LLM reasoning layer for explanations and Q&A:

pip install "skforecast-ai[llm]"

For development or contributing:

git clone https://github.com/skforecast/skforecast-ai.git
cd skforecast-ai
pip install -e ".[dev]"

Smoke test

Run the snippet below. If it prints a predictions table and a metrics row, the installation is working.

import pandas as pd
from skforecast_ai import ForecastingAssistant
from skforecast.datasets import load_demo_dataset

data = load_demo_dataset(verbose=False)
assistant = ForecastingAssistant(llm=None)
result = assistant.forecast(data=data, target="y", steps=12, test_size=12)

print(result.predictions)   # forecast for the held-out test window
print(result.metrics)       # evaluation metrics: MAE, MSE, MASE
print(result.code)          # the skforecast script that produced this result

Runs locally by default

The smoke test runs in deterministic mode: no LLM, no network access, and no configuration required.

Next steps