More about forecasting in cienciadedatos.net

• Time series forecasting with machine learning
• ARIMA and SARIMAX models
• Time series forecasting with gradient boosting
• Exponential smoothing models
• ARAR forecasting models
• Visualizing time series data
• Electricity demand forecasting
• Global forecasting models I
• Global forecasting models II
• Global forecasting models III
• Clustering time series
• Forecasting with deep learning models
• Probabilistic forecasting I
• Probabilistic forecasting II
• Probabilistic forecasting III
• Data drift detection in time series forecasting models
• Forecasting categorical time series
• Interpretability and explainability in forecasting models

Skforecast Studio¶

Skforecast Studio is an interactive, no-code application to build time series forecasting models visually, while automatically generating production-ready Python code using skforecast.

Whether you're exploring data, prototyping models, or generating deployment-ready code, Skforecast Studio helps you go from raw time series to forecasting pipeline in minutes, no coding required.

Key Features¶

• Visual model building — Configure forecasters, lags, transformers, and exogenous variables through an intuitive interface.
• Auto-generated Python code — Every step produces clean, reproducible skforecast code ready for production.
• Interactive data exploration — Visualize your time series, inspect seasonality, and detect patterns before modeling.
• Backtesting and evaluation — Evaluate model performance with built-in backtesting and metrics.
• No installation needed — Runs directly in your browser at studio.skforecast.org.

Who is it for?¶

• Data scientists who want to prototype forecasting models faster.
• Analysts and domain experts who need forecasting capabilities without writing code.
• Teams looking for a collaborative way to explore and validate time series models.
• Students and educators learning time series forecasting with a hands-on tool.

Try it now¶

Launch Skforecast Studio

Feedback and Issues¶

Skforecast Studio is developed by the skforecast team. If you encounter any issues or have suggestions, please open an issue using the Skforecast Studio issue template.

Citation¶

How to cite this document

If you use this document or any part of it, please acknowledge the source, thank you!

Forecasting with statistical models by Joaquín Amat Rodrigo, Javier Escobar Ortiz and Resul Akay available under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0 DEED) at https://cienciadedatos.net/documentos/py77-forecasting-statistical-models.html

How to cite skforecast

If you use skforecast for a publication, we would appreciate if you cite the published software.

Zenodo:

Amat Rodrigo, Joaquin, & Escobar Ortiz, Javier. (2024). skforecast (v0.20.0). Zenodo. https://doi.org/10.5281/zenodo.8382787

APA:

Amat Rodrigo, J., & Escobar Ortiz, J. (2024). skforecast (Version 0.20.0) [Computer software]. https://doi.org/10.5281/zenodo.8382787

BibTeX:

@software{skforecast, author = {Amat Rodrigo, Joaquin and Escobar Ortiz, Javier}, title = {skforecast}, version = {0.20.0}, month = {01}, year = {2026}, license = {BSD-3-Clause}, url = {https://skforecast.org/}, doi = {10.5281/zenodo.8382788} }

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This work by Joaquín Amat Rodrigo, Javier Escobar Ortiz is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International.

Allowed:

• Share: copy and redistribute the material in any medium or format.

• Adapt: remix, transform, and build upon the material.

Under the following terms:

• Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

• NonCommercial: You may not use the material for commercial purposes.

• ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.