Skforecast AI¶

Skforecast AI is an AI-assisted forecasting package from the skforecast team. It combines a deterministic forecasting engine powered by skforecast with an optional LLM reasoning layer.

Provide a time series and the assistant can profile the data, choose a forecasting strategy using established best practices, evaluate its performance, and return both the forecast and the runnable skforecast code that produced it.

Why Skforecast AI?¶

Deterministic by design: The rule-based forecasting engine produces consistent results for the same input.
Inspectable and reproducible: The generated script is the code that ran, so you can inspect, version, and execute it independently with skforecast.
From data to forecast in one call: Automates profiling, model and estimator selection, feature engineering, and backtesting.
Python and CLI workflows: Use the assistant from Python or run the complete pipeline from the terminal.
Optional LLM reasoning: Get plain-language explanations and configuration advice while keeping the core forecasting workflow available offline.
Built on skforecast: Supports recursive and direct forecasters, multi-series forecasting, statistical models, and foundation models.

Installation¶

Skforecast AI requires Python 3.10 or later.


pip install skforecast-ai

Install the optional LLM reasoning layer with:


pip install "skforecast-ai[llm]"

Quick Start¶


from skforecast.datasets import load_demo_dataset
from skforecast_ai import ForecastingAssistant

data = load_demo_dataset(verbose=False)
assistant = ForecastingAssistant()
result = assistant.forecast(data=data, target="y", steps=12)

print(result.predictions)
print(result.metrics)
print(result.code)

The result includes the predictions, backtesting metrics, data profile, selected modeling plan, and the standalone skforecast script used to produce the forecast.

Learn More¶

Documentation: Tutorials, user guides, API reference, and release notes.
Quick start: Create your first AI-assisted forecast.
Introduction to agentic forecasting: Learn how the deterministic engine and optional reasoning layer work together.
GitHub repository: Browse the source, report issues, and contribute.

Feedback and Issues¶

Skforecast AI is developed by the skforecast team. If you encounter a problem or have a suggestion, please open an issue.