MCP Server
skforecast-ai
Forecast time series in CSV files: deterministic skforecast workflows and the script that ran.
- Data Science & ML
- By skforecast
Features
- 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.
- 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.
- 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.
Frequently asked questions
What does skforecast-ai do?
Forecast time series in CSV files: deterministic skforecast workflows and the script that ran.
Is skforecast-ai open source?
Yes — skforecast's source code is publicly available on GitHub, linked above.
What category is skforecast-ai in?
skforecast-ai is listed under Data Science & ML.
Who maintains skforecast-ai?
skforecast-ai is built and maintained by skforecast.