Prophet
greykite
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| Prophet | greykite | |
|---|---|---|
| 214 | 3 | |
| 15,904 | 1,702 | |
| 1.5% | 1.2% | |
| 8.5 | 5.8 | |
| 9 days ago | 6 days ago | |
| Python | Python | |
| MIT License | BSD 2-clause "Simplified" License |
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Prophet
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Complete: D214 - MSDA Capstone
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
- Ask HN: Data Scientists, what libraries do you use for timeseries forecasting?
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[D] Time Series Question
Prophet
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LSTM/CNN architectures for time series forecasting[Discussion]
Prophet
- Eden
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Predição de ações na bolsa de valores com Python e Facebook Prophet
Prophet: Automação preditiva.
- Time series analysis of Bitcoin price in Python with fbprophet ?!
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Data Science toolset summary from 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
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Personal Support at Internet Scale
We run an anomaly detection app powered by Facebook's Prophet forecasting library. It tells us if metrics dip or rise in unexpected ways ("Did signups drop? Is something broken with that flow?"). We built the service because customers kept reaching out to tell us some feature broke before we noticed. Normally these issues show up in product data, so the app looks for these anomalies and tells us when they happen.
greykite
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Hello reddit, what time series forecasting tools are you using?
I've been using greykite for forecasting some business metrics lately.
- Darts: Non-Facebook alternative for timeseries forecasting
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Predicting Daily Sales
Some other options to potentially look into are Facebook's Prophet and one of my new favorites, Greykite. These have some very useful functions to automatically fit seasonality and holidays. They also have the flexibility allowing you to custom define holiday periods (think times when certain promotions or campaigns were running) and other regressors (think macroeconomic data that may have a material effect on your sales).
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.
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
scikit-learn - scikit-learn: machine learning in Python
MLflow - Open source platform for the machine learning lifecycle
sktime - A unified framework for machine learning with time series
Keras - Deep Learning for humans
pytorch-forecasting - Time series forecasting with PyTorch
Robyn - Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.
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.
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.