Forecasting Python Packages

statsmodels

Statsmodels: statistical modeling and econometrics in Python

prophet

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

pmdarima

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

arch

ARCH models in Python

sktime

A unified framework for machine learning with time series

chronos-forecasting

Chronos: Pretrained Models for Time Series Forecasting

mlforecast

Scalable machine 🤖 learning for time series forecasting.

gluonts

Probabilistic time series modeling in Python

autogluon-core

Fast and Accurate ML in 3 Lines of Code

autogluon-features

Fast and Accurate ML in 3 Lines of Code

autogluon-tabular

Fast and Accurate ML in 3 Lines of Code

autogluon-common

Fast and Accurate ML in 3 Lines of Code

hierarchicalforecast

Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.

darts

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

autogluon-timeseries

Fast and Accurate ML in 3 Lines of Code

autogluon

Fast and Accurate ML in 3 Lines of Code

neuralforecast

Scalable and user friendly neural :brain: forecasting algorithms.

autogluon-multimodal

Fast and Accurate ML in 3 Lines of Code

skforecast

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

fev

Forecast evaluation library

tsdb

a Python toolbox loads 173 public time series datasets for machine/deep learning with a single line of code. Datasets from multiple domains including healthcare, financial, power, traffic, weather, and etc.

forecasting-tools

A framework for building a AI Forecasting Bot for Metaculus. Additionally AI Forecasting tools to help humans forecast the future.

pypots

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values