Scalable machine 馃 learning for time series forecasting.
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Scalable and user friendly neural :brain: forecasting algorithms.
Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
Time series forecasting with PyTorch
Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.
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 馃殌.
