Prophet VS scikit-learn

Compare Prophet vs scikit-learn and see what are their differences.

Prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. (by facebook)
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Prophet scikit-learn
221 81
17,720 57,985
1.0% 0.9%
6.2 9.9
16 days ago 6 days ago
Python Python
MIT License BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Prophet

Posts with mentions or reviews of Prophet. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-25.

scikit-learn

Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-04-09.

What are some alternatives?

When comparing Prophet and scikit-learn you can also consider the following projects:

tensorflow - An Open Source Machine Learning Framework for Everyone

Surprise - A Python scikit for building and analyzing recommender systems

darts - A python library for user-friendly forecasting and anomaly detection on time series.

Keras - Deep Learning for humans

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

greykite - A flexible, intuitive and fast forecasting library

gensim - Topic Modelling for Humans

MLflow - Open source platform for the machine learning lifecycle

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

PyBrain