ForecastFlowML Docs#

ForecastFlowML is a scalable machine learning forecasting framework that enables parallel training (by distributing models rather than data) of scikit-learn like models based on PySpark.

With ForecastFlowMl, you can build scikit-learn like regressors as direct multi-step forecasters, and train a seperate model for each group in your dataset. Our package leverages the power of PySpark to efficiently handle large datasets and enables distributed computing for faster model training.

Features#

ForecastFlowML provides a range of features that make it a powerful and flexible tool for time-series forecasting, including:

  • Works with Pandas and Pyspark DataFrames.

  • Distributed model training per group in the PySpark/Pandas DataFrames.

  • Direct multi-step forecasting.

  • Built-in time based cross-validation,

  • Extensive time-series feature engineering (lag, rollin.g mean/std, stockout, history length).

  • Hyperparameter tuning for each group model with grid search.

  • Supports scikit-learn like libraries such as LightGBM or XGBoost.

Whether you’re new to time-series forecasting or an experienced data scientist, ForecastFlowML can help you build and deploy accurate forecasting models at scale.

Installation#

You can install the packaging using the following command.

pip install forecastflowml