azul

### Description Currently, `statsforecast` can generate mean forecasts and prediction intervals. However, it could be more beneficial to provide the option of returning the forecast distribution for each timestamp within...

[Models, AutoARIMA] Match `statsforecast.arima` and `statsforecast.models` AutoARIMA

1

### Description _No response_ ### Use case _No response_

[Core] Add support for polars

1

### Description Our codebase primarily relies on pandas for data handling and manipulation tasks. However, we have identified potential performance improvements that could be gained by incorporating support for [Polars](https://github.com/pola-rs/polars),...

[FEAT] Explore AnyDataframe (spark, ray, and dask) integration

3

Currently, hierarchicalforecast only supports a pandas dataframe as input. For the library to scale horizontally, we need to explore different alternatives on how to integrate frameworks such as spark, ray,...

In some cases, `utils.aggregate` returns a different order of `unique_id` in `Y_df` and `S`. Even though `core.HierarchicalReconciliation` ensures the same order, it could reduce a good user experience.

[FIX] Improve `Introduction to Hierarchical Forecasting`

1

The `Base predictions` and `Reconciliation` sections don't have a complete explanation of the hierarchical reconciliation methodology. Ideas to improve those sections: Base predictions: - Define what we mean by "base...

[FIX] Recover `Non-Negative Hierarchical Reconciliation` instead of `Non-Negative MinTrace`

2

The section `Non-negative MinTrace` is not as informative as `Non-Negative Hierarchical Reconciliation`, especially for potential users unfamiliar with the `MinTrace` method.

[FEAT] ESRNN Model

1

A user requested to include the ESRNN model (available in previous versions of NeuralForecast). The user tried to use [this](https://github.com/kdgutier/esrnn_torch) version but it does not allow more than one exogenous...

### Description `scale=True` is the default parameter that miscalculates `sparsity` in general by default. a warning or error should be raised. ### Use case _No response_

[Core] Make the library compatible with AnyDataframe (spark, ray, dask)

2

### Description Currently, `tsfeatures` utilizes a map-reduce approach and multiprocessing to compute several features for different time series. However, the implementation is currently only supported for pandas. By incorporating [fugue](https://github.com/fugue-project/fugue),...