Prophet Alternatives
Similar projects and alternatives to Prophet
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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
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Activeloop.ai
Optimize your datasets for ML. Goodbye, boilerplate code - the fastest dataset optimization and management tool for computer vision.
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Robyn
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) code from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, 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 t
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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.
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Pattern
Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.
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EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of
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mljar-supervised
Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning :rocket:
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Reviews and mentions
- ASK: Predicting Time Series Data
- How to forecast resource utilization for a hospital using R?
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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.
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A Regression with time series data
There is a whole field of time series forecasting so there are a lot of models that you can use. ARIMA, Exponential smoothing are some of the classic ones. If you are just looking for some quick results your best bet is using something like prophet .
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Deeney: "I get 30-40 racist messages a week, easily. On pictures of me, my missus, my kids. And I'm a marmite individual, some like me some don't, you can talk about my footballing ability as much as you want - I just don't understand why you have to talk about the colour of my skin."
These companies will tell advertisers that their data science teams and their algorithms are the best in the world. They hire some of the best and brightest minds, and their clustering algorithms and forecast libraries are some top-tier solutions.
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[D] Best books for learning time series analysis/forecasting in python
For tools, look at http://alkaline-ml.com/pmdarima/ and https://facebook.github.io/prophet/
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Any Marketing data scientist/analyst here?
Facebook Prophet seems to be decent.
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Any Marketing data scientist out here?
Forecasting - Prophet and greykite are two libraries that are quite in vogue right now. You'll definitely need some domain knowledge to fit the forecast, these really shouldn't be used to just fit and go otherwise you'll probably end up with some bad results. You can also look at traditional forecasting approaches - ARIMA, SARIMA, ETS, VARs etc. If you're interested in these sort of approaches, check out Forecasting: Principles and Practices. It is basically forecasting gospel.
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A python library for easy manipulation and forecasting of time series.
This is cool. I also like using Facebook's open source package Prophet
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Darts: Non-Facebook alternative for timeseries forecasting
> I suggest moving the Prophet, Torch, and Pmdarima dependencies to setuptools "extras",
Yep, the dependence on Prophet prevented me to quickly try darts on a Windows machine because of the pystan issue with prophet:
https://github.com/facebook/prophet/issues/732
May try it later on Linux.
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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).
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[D] Unfair Comparison: Neural Networks vs Taylor Polynomials and Fourier Series
Trigonometric polynomials are still used all the time in domains where periodic behavior is expected. As just one example, the very popular prophet forecasting library uses sinusoidal expansion of a time series as one of its core features.
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Does anyone know of a way to include external factors (explanatory variables) into time series with multiple seasonalities?
Maybe check out Prophet or Greykite.
Stats
facebook/prophet is an open source project licensed under MIT License which is an OSI approved license.
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