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A flexible, intuitive and fast forecasting library (By LinkedIn)
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What is Greykite?

It is a forecast library that allows you to do exploratory data analysis (EDA), forecast pipeline, model tuning, benchmarking, etc. It includes the Silverkite model, a forecast model developed by Linkedin, which allows feature engineering, automatic changepoint detection, holiday effects, various machine learning fitting methods, statitical prediction bands, etc.
Greykite is a tool in the Machine Learning Tools category of a tech stack.
Greykite is an open source tool with 1.7K GitHub stars and 89 GitHub forks. Here’s a link to Greykite's open source repository on GitHub

Who uses Greykite?


Greykite Integrations

Greykite's Features

  • Provides time series regressors to capture trend, seasonality, holidays, changepoints, and autoregression, and lets you add your own
  • Fits the forecast using a machine learning model of your choice
  • Provides powerful plotting tools to explore seasonality, interactions, changepoints, etc
  • Provides model templates (default parameters) that work well based on data characteristics and forecast requirements (e.g. daily long-term forecast)
  • Produces interpretable output, with model summary to examine individual regressors, and component plots to visually inspect the combined effect of related regressors
  • Facilitates interactive prototyping, grid search, and benchmarking. Grid search is useful for model selection and semi-automatic forecasting of multiple metrics
  • Exposes multiple forecast algorithms in the same interface, making it easy to try algorithms from different libraries and compare results
  • The same pipeline provides preprocessing, cross-validation, backtest, forecast, and evaluation with any algorithm

Greykite Alternatives & Comparisons

What are some alternatives to Greykite?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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