Forecasting examples in Python

This folder contains Jupyter notebooks with Python examples for building forecasting solutions. To run the notebooks, please ensure your environment is set up with required dependencies by following instructions in the Setup guide.

Summary

The following summarizes each directory of the Python best practice notebooks.

Directory	Content	Description
00_quick_start	autoarima_single_round.ipynb azure_automl_single_round.ipynb lightgbm_single_round.ipynb	Quick start notebooks that demonstrate workflow of developing a forecasting model using one-round training and testing data
01_prepare_data	ojdata_exploration.ipynb ojdata_preparation.ipynb	Data exploration and preparation notebooks
02_model	dilatedcnn_multi_round.ipynb lightgbm_multi_round.ipynb autoarima_multi_round.ipynb	Deep dive notebooks that perform multi-round training and testing of various classical and deep learning forecast algorithms
03_model_tune_deploy	azure_hyperdrive_lightgbm.ipynb aml_scripts/	<ul><li> Example notebook for model tuning using Azure Machine Learning Service and deploying the best model on Azure </ul></li> <ul><li> Scripts for model training and validation </ul></li>