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nixtlats reviews and mentions
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Automated Time Series Processing and Forecasting
Users can use their own models. Just create a fork of the repo and make the appropriate modifications to include any model the user wants to deploy. On our side, we are working to include Deep Learning models with the nixtlats library (https://github.com/nixtla/nixtlats/) that we also developed.
About benchmarking using statistical models, we highly recommend using statsforecast (https://github.com/Nixtla/statsforecast) that we created. It is designed to be highly efficient in fitting statistical models on millions of time series. More complex models can be built on the results to get a positive Forecast Value Added.
Stats
Nixtla/nixtlats is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of nixtlats is Jupyter Notebook.
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