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statsforecast
Lightning ⚡️ fast forecasting with statistical and econometric models.neuralforecast
Scalable and user friendly neural 🧠 forecasting algorithms.nixtla
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 🚀.hierarchicalforecast
Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.mlforecast
Scalable machine 🤖 learning for time series forecasting.tsfeatures
Calculates various features from time series data. Python implementation of the R package tsfeatures.Nixtla
Automated time series processing and forecasting.transfer-learning-time-series
Transfer 🤗 Learning for Time Series Forecastingdatasetsforecast
Datasets for time series forecastingfpp3-python
Forecasting: principles and practice in pythontimegpt-forecaster-streamlit
TimeGPT forecaster example using streamlitvantage
Use TimeGPT to predict cloud costs and detect anomalies.public-slides
Nixtla Public Slidesnixtlats
popol-vuh
Popol Vuh: Nixtla's operating systemutilsforecast
m4-forecasts
ZIP version of M4 forecasts uploaded to https://github.com/Mcompetitions/M4-methods/tree/master/Point%20Forecasts.m5-forecasts
ZIP version of dataset and forecasts uploaded to https://drive.google.com/drive/folders/1D6EWdVSaOtrP1LEFh1REjI3vej6iUS_4.nixtla-commons
Nixtla shared assetsblog
docs
how-to-contribute-nixtlaverse
Instruction to contribute to the Nixtla librariesLove Open Source and this site? Check out how you can help us