Stock-Market-Prediction vs Prophet

Stock-Market-Prediction

This project aims to predict future values on the stock market for a stock using projections with facebook prophet. (by LucasDiogo96)

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Source Code

medium.com

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Prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. (by facebook)

Machine Learning Forecasting R Python

Source Code

facebook.github.io

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Stock-Market-Prediction		Prophet
	Project
1	Mentions	221
5	Stars	17,767
-	Growth	0.5%
0.0	Activity	6.2
about 2 years ago	Latest Commit	1 day ago
Jupyter Notebook	Language	Python
-	License	MIT 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.

Stock-Market-Prediction

Posts with mentions or reviews of Stock-Market-Prediction. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-03-23.

Predição de ações na bolsa de valores com Python e Facebook Prophet
5 projects | dev.to | 23 Mar 2022

O código completo pode ser encontrado no meu Github: GitHub — LucasDiogo96/Stock-Market-Prediction

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.

Moirai: A Time Series Foundation Model for Universal Forecasting
2 projects | news.ycombinator.com | 25 Mar 2024

https://facebook.github.io/prophet/
"Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well."
prophet: NEW Data - star count:17116.0
1 project | /r/algoprojects | 10 Nov 2023

1 project | /r/algoprojects | 9 Nov 2023
prophet: NEW Data - star count:17082.0
1 project | /r/algoprojects | 7 Nov 2023
Facebook Prophet: library for generating forecasts from any time series data
7 projects | news.ycombinator.com | 26 Sep 2023
prophet: NEW Data - star count:16196.0
1 project | /r/algoprojects | 12 Aug 2023

1 project | /r/algoprojects | 11 Aug 2023
prophet: NEW Data - star count:15889.0
1 project | /r/algoprojects | 27 May 2023

1 project | /r/algoprojects | 26 May 2023

1 project | /r/algoprojects | 25 May 2023

What are some alternatives?

When comparing Stock-Market-Prediction and Prophet you can also consider the following projects:

pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators

tensorflow - An Open Source Machine Learning Framework for Everyone

Yahooquery - Python wrapper for an unofficial Yahoo Finance API

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

scikit-learn - scikit-learn: machine learning in Python

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

MLflow - Open source platform for the machine learning lifecycle

Keras - Deep Learning for humans

sktime - A unified framework for machine learning with time series

pytorch-forecasting - Time series forecasting with PyTorch

Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.