Facebook Prophet: library for generating forecasts from any time series data

• statsforecast

  58 3,540 8.9 Python

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  

Model development on Prophet stopped this year: https://medium.com/@cuongduong_35162/facebook-prophet-in-202...

They recommend checking out these for cutting-edge time series forecasting:

https://neuralprophet.com/

https://nixtla.github.io/statsforecast/

• Prophet

  221 17,743 6.2 Python

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

  

• InfluxDB

  www.influxdata.com sponsored

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• hamilton

  20 1,312 9.8 Jupyter Notebook

  Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.

  

This library is old news? Is there anything new that they've added that's noteworthy to take it for another spin?

[disclaimer I'm a maintainer of Hamilton] Otherwise FYI Prophet gels well with https://github.com/DAGWorks-Inc/hamilton for setting up your features and dataset for fitting & prediction[/disclaimer].

• neural_prophet

  5 3,635 8.6 Python

  NeuralProphet: A simple forecasting package

• darts

  47 7,272 9.1 Python

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

  

As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.

The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).

That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).

• minGPT

  34 18,789 0.0 Python

  A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

Tried it once. Its promise is to take the dataset's seasonal trend into account, which makes sense for Facebook's original use case.

We ran it on such a dataset and found out that directly using https://github.com/karpathy/minGPT consistently gives a better result. So we ended up using the output of Prophet as an input feature to a neural network, but the result was not improved in any significant way.

Suggest a related project