Time Series Forecasting Best Practices & Examples

artificial-intelligence automl azure-ml best-practices deep-learning demand-forecasting dilated-cnn forecasting hyperparameter-tuning jupyter-notebook lightgbm machine-learning model-deployment prophet python r retail tidyverse time-series

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README.md

Forecasting Best Practices

This repository contains examples and best practices for building Forecasting solutions and systems, provided as Jupyter notebooks and a library of utility functions. The focus of the repository is on state-of-the-art methods and common scenarios that are popular among researchers and practitioners working on forecasting problems.

Getting Started

To get started, navigate to the Setup Guide, which lists instructions on how to set up your environment and dependencies, download the data and run examples provided in the repository.

Contributing

We hope that the open source community would contribute to the content and bring in the latest SOTA algorithm. This project welcomes contributions and suggestions. Before contributing, please see our Contributing Guide.

Build Status

Build	Branch	Status
Linux CPU	master
Linux CPU	staging