Developed by Facebook, Prophet is a robust library for time series forecasting. It's designed to handle daily observations that display patterns on different time scales.

Statsmodels is a comprehensive library for statistical modeling. It provides tools for time series analysis, including ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal ARIMA) models.

TensorFlow Time Series (TFTS) is an extension of TensorFlow designed specifically for time series analysis. It includes modules for building and training neural network models for forecasting.

Kats, short for Kit for Automated Time Series, is a powerful Python library designed to simplify and automate time series forecasting tasks. Developed by Facebook, Kats offers a comprehensive set of tools and models for analyzing time series data, making it particularly useful for automated forecasting. It provides users with the ability to apply state-of-the-art models, conduct feature engineering, and explore different forecasting techniques, contributing to an efficient and automated approach to time series analysis.

tsfresh is a Python library designed for automated feature extraction from time series data. It streamlines the process of extracting a diverse set of interpretable features from both univariate and multivariate time series datasets. With a comprehensive range of features, including statistical measures and characteristic values, tsfresh is a valuable tool for enhancing time series data for machine learning tasks.

GluonTS is a deep learning library developed by Amazon, built on Apache MXNet, for time series forecasting. It excels in probabilistic forecasting, allowing the modeling of uncertainties in predictions. With support for custom deep learning model architectures and integration with MXNet for scalable training, GluonTS is ideal for tasks requiring accurate and probabilistic forecasting, such as demand forecasting and financial time series prediction.