Mastering Time Series Forecasting: A Guide to Python’s Most Influential Libraries

The Python ecosystem offers a rich suite of libraries for time series forecasting. Each caters to different needs and comes with its community and popularity, often reflected in the number of GitHub stars. Here’s a rundown of the top libraries, their best use cases, and resources for learning more:

  1. Prophet (Facebook):
  1. pmdarima:
  1. Skforecast:
  1. Greykite (LinkedIn):
  1. Functime:
  1. Arch:

Nixtla’s Suite:

  • StatsForecast:
  • Best for: Rapid computations and high-performance univariate time series forecasting.
  • GitHub Stars: Check Latest
  • Best Article: Nixtla Official Page
  • mlforecast:
  • Best for: Distributed computing environments needing feature engineering at scale.
  • GitHub Stars: Check Latest
  • NeuralForecast:
  • Best for: Leveraging neural networks for time series forecasting, suitable for non-experts.
  • GitHub Stars: Check Latest

Transformers for Time Series:

This curated guide aims to illuminate the path for those exploring the varied landscape of time series forecasting, providing a compass to the tools that resonate most with your project.