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|MIT License||Apache License 2.0|
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Facebook Prophet: library for generating forecasts from any time series data
7 projects | news.ycombinator.com | 26 Sep 2023
Complete: D214 - MSDA Capstone
4 projects | /r/WGU_MSDA | 15 Mar 2023
My rescue came from discovering some of the alternatives to ARIMA/SARIMA, which was the extent of what we had covered for time series data. A series of searches eventually led me to some automated time series analysis packages, one of which was Prophet, an open source time series package released by Facebook's core data science team. This was a life saver, being a much more efficient and more effective forecasting tool than sloooowly iterating through ARIMA/SARIMA models that seemed to want to fight with me. If you're going to do a time series analysis for your capstone, I strongly suggest taking a look at using Prophet.
Dec 12, 2022 FLiP Stack Weekly
20 projects | dev.to | 11 Dec 2022
Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
7 projects | news.ycombinator.com | 3 Nov 2022
[D] Time Series Question
2 projects | /r/MachineLearning | 25 Sep 2022
LSTM/CNN architectures for time series forecasting[Discussion]
3 projects | /r/MachineLearning | 6 May 2022
16 projects | news.ycombinator.com | 12 Apr 2022
Predição de ações na bolsa de valores com Python e Facebook Prophet
5 projects | dev.to | 23 Mar 2022
Prophet: Automação preditiva.
Time series analysis of Bitcoin price in Python with fbprophet ?!
2 projects | dev.to | 22 Dec 2021
Data Science toolset summary from 2021
13 projects | dev.to | 13 Nov 2021
Prophet - It is a time-series forecasting library built by Facebook. 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. Link - https://github.com/facebook/prophet
PSA: You don't need fancy stuff to do good work.
10 projects | /r/datascience | 9 May 2023
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.
xgboost VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
darts - A python library for user-friendly forecasting and anomaly detection on time series.
scikit-learn - scikit-learn: machine learning in Python
greykite - A flexible, intuitive and fast forecasting library
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
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
mlpack - mlpack: a fast, header-only C++ machine learning library