In today's exciting release😎

Use @nixtlainc's StatsForecast to beat a WIDE variety of #DeepLearning models with:

- interpretable methods 📈
- in under 10 min ⏲️
- $0.5c in AWS 🔥
- and a few lines of #python code 🤯

#reinventforecasting

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Building upon the great work of @spyrosmakrid et al, we fitted an ensemble of simple statistical models:

AutoARIMA, Exponential Smoothing (@robjhyndman etal),

Complex Exponential Smoothing (@iSvetunkov etal)

and the DOT method (@fotpetr etal)

The results were great!🔥

Today at @nixtlainc: fast and interpretable forecasting for multiple seasonalities. ⏲️🔎

This is exciting news for data scientists in the Python ecosystem. 🧵
#reinventforecasting Time series are the operational DNA of the world. 🧬

Most business data (sales, demand, electricity load, sensors) is stored in tabular format with time indexes.

Most forecasting models in Python are only suited for single seasonal data. ⚠️
#reinventforecasting

@nixtlainc started last year as a side project

Today we reached 1 million downloads🎉

Our goal is to shake the time series industry and make state-of-the-art algorithms available for everyone 🔥

This is how we got there

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#reinventforecasting @nixtlainc's ecosystem consists (now) of 5 #python libraries

Focus: speed, scalability, and accuracy 🚀

Some features:
* @scikit_learn syntax
* Native support for #spark, @raydistributed, and @dask_dev
* Models! Eg #arima, #LightGBM, #NeuralNetworks, #transformers 🤖

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