skforecast/skforecast-ai

About skforecast-ai

The forecasting assistant that shows its work. Give skforecast-ai a time series: it profiles the data, chooses the model with deterministic rules, validates it and returns the forecast together with the skforecast script that produced it. Every decision comes from a rule you can read, the same data always gives the same result, and the script runs on its own with plain skforecast. An optional LLM explains every decision; it never makes them.

36-hour LightGBM forecast of hourly bike sharing users, with its 80% prediction interval against the held-out hours, and the answer of ask() that explains its metrics. Real skforecast-ai outputs, from the animation on ai.skforecast.org.

Installation

pip install skforecast-ai

The LLM layer is optional. To use it, install the extra ([bedrock] for AWS Bedrock):

pip install "skforecast-ai[llm]"

Requires Python 3.10 or newer. More options in the installation guide; to install from source, see the Contribution Guide.

Quick example

Try it without installing anything: Your first forecast runs in Google Colab, from the data to a validated forecast, with no API key.

from skforecast_ai import ForecastingAssistant
from skforecast.datasets import load_demo_dataset

# Download demo dataset (monthly)
data = load_demo_dataset(verbose=False)

# Profile the data, plan the model, generate the script and run it,
# evaluated on the last 12 months. No LLM needed.
assistant = ForecastingAssistant()
result = assistant.forecast(data=data, target="y", steps=12, test_size=12)

result.predictions.head()
#                 pred
# 2007-07-01  0.851486
# 2007-08-01  1.048000
# 2007-09-01  0.998147
# 2007-10-01  1.147577
# 2007-11-01  1.110845

result.metrics
#   series       MAE       MSE      MASE      MAPE
# 0      y  0.078442  0.008933  0.802828  0.088608

print(result.code)  # the skforecast script that produced this result

That single call chose a ForecasterRecursive with a Ridge estimator, its lags and window features, and ran the script that result.code returns. result.profile and result.plan hold every decision and the rule behind it. Drop test_size to train on all the data and forecast the next 12 months.

Features

A deterministic engine

• Data profiling: frequency, missing values, exogenous and categorical variables, and the significant lags from the partial autocorrelation.
• A plan with a rule for every decision: forecaster and estimator, lags, window and calendar features, prediction intervals, metric and cross-validation.
• The code you see is the code that ran: forecast() and backtest() execute the same script that forecast_code() and backtest_code() return. Inspect it, version it or run it with plain skforecast.
• Model selection with compare(): every candidate is backtested with the same data and cross-validation, and the ranking is a plain sort of the metric. For a single series, a seasonal naive baseline is ranked alongside them, so you also see whether a model beats the simplest reasonable forecast.
• Reproducible: the same input always gives the same profile, plan, script and predictions.
• Python or terminal: the CLI runs the same pipeline from a CSV file or URL.
• Coding agents: an MCP server gives Claude Code, Cursor and other MCP clients the same pipeline as tools, with a skill that teaches them to use it. Responses never carry rows of your data.

The engine chooses among the forecasters of skforecast: recursive and direct, multi-series and multivariate, statistical (ARIMA) and foundation models.

An optional LLM layer

• Explains a forecast, a backtest, a comparison, a plan or a script in plain language with ask(), or answers a general forecasting question.
• Refines lags and window features from your domain knowledge with refine_plan(prompt=...).
• Translates a deployment scenario into a backtesting strategy with create_cv(prompt=...).
• Validated before it runs: every LLM output is checked by a Pydantic model; if it is not valid, you get the deterministic result and a warning.
• Any provider through pydantic-ai: OpenAI, Anthropic, Google, Groq, AWS Bedrock, Ollama for local models, or any OpenAI-compatible endpoint.

Methods

All methods work without an LLM except ask(). A prompt is always explicit: nothing is sent to an LLM unless you ask for it.

How it works

skforecast-ai has two ways in, on the same engine. The fast path goes from data to a forecast or a backtest in one call. The step-by-step path returns each intermediate object (profile, plan, cross-validation) so you can inspect it, change it or refine it with the LLM before running anything. Both branch into forecasting and backtesting, compare() picks the best configuration from measured performance, and ask() explains any object along the way.

Read more in Agentic forecasting and Agentic forecasting step by step.

Documentation

The full documentation is available at https://ai.skforecast.org.

Part of the skforecast family

• skforecast: the forecasting library that runs every forecast of skforecast-ai. Machine learning, statistical and foundation models, backtesting and tuning.
• Skforecast Studio: a no-code application to build forecasting models visually, which generates production-ready Python code.

Contributing

Bug reports, feature requests, code, tests and documentation are all welcome. Open an issue on GitHub Issues or read the Contribution Guide and the Code of Conduct to get started.

skforecast-ai is created and maintained by Joaquín Amat Rodrigo and Javier Escobar Ortiz, together with everyone who has contributed to it (about the project).

Citation

If you use skforecast-ai in a scientific publication, please cite the version you used: each version has its own DOI and ready-made citations on Zenodo. To cite skforecast-ai in general, use the DOI that always resolves to the latest release.

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. skforecast-ai [Computer software]. https://doi.org/10.5281/zenodo.21338159

BibTeX:

@software{skforecast-ai,
  author  = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier},
  title   = {skforecast-ai},
  license = {Apache-2.0},
  url     = {https://ai.skforecast.org/},
  doi     = {10.5281/zenodo.21338159}
}

The citation metadata is also in CITATION.cff (GitHub’s “Cite this repository” button).

Sponsorship and funding

skforecast-ai is built by the skforecast team. skforecast is free, open-source software supported by the Sovereign Tech Fund and by the organizations that sponsor it. If your company relies on skforecast or skforecast-ai, see Sponsorship and funding for sponsorship tiers, support agreements, feature sponsorship and training.

Individuals can support the project through Open Collective, Buy Me a Coffee, GitHub Sponsors (Joaquín Amat Rodrigo, Javier Escobar Ortiz) or PayPal.

   

License

skforecast-ai software: Apache License 2.0. The underlying skforecast engine is distributed under its own BSD-3-Clause License.

skforecast-ai documentation: CC BY-NC-SA 4.0

Trademark: The trademark skforecast is registered with the European Union Intellectual Property Office (EUIPO) under the application number 019109684. Unauthorized use of this trademark, its logo, or any associated visual identity elements is strictly prohibited without the express consent of the owner.