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

Statsmodels: statistical modeling and econometrics in Python

Fast and Accurate ML in 3 Lines of Code

A unified framework for machine learning with time series

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

Postgres with GPUs for ML/AI apps.

Chronos: Pretrained Models for Time Series Forecasting

Probabilistic time series modeling in Python

Time series forecasting with PyTorch

Lightning ⚡️ fast forecasting with statistical and econometric models.

Scalable and user friendly neural :brain: forecasting algorithms.

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

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

ARCH models in Python

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

Scalable machine 🤖 learning for time series forecasting.

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Time series analysis in the `tidyverse`

Distributional crypto-return forecasting via Wasserstein-geodesic extrapolation in quantile-function space. WGeo family wins 12/12 (asset × horizon) cells over 6.75y walk-forward CRPS vs GARCH and classical baselines. v0.4.