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 Datasets & Benchmarks category of a tech stack.
Key Features
Provides time series regressors to capture trend, seasonality, holidays, changepoints, and autoregression, and lets you add your ownFits the forecast using a machine learning model of your choiceProvides powerful plotting tools to explore seasonality, interactions, changepoints, etcProvides 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 regressorsFacilitates interactive prototyping, grid search, and benchmarking. Grid search is useful for model selection and semi-automatic forecasting of multiple metricsExposes multiple forecast algorithms in the same interface, making it easy to try algorithms from different libraries and compare resultsThe same pipeline provides preprocessing, cross-validation, backtest, forecast, and evaluation with any algorithm
Greykite Pros & Cons
Pros of Greykite
No pros listed yet.
Cons of Greykite
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Greykite Alternatives & Comparisons
What are some alternatives to Greykite?
TensorFlow
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
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
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
