Hierarchical Forecasting in Python | Nixtla

A vast amount of time series datasets are organized into structures with different levels or hierarchies of aggregation.
In this talk, we introduce the open-source Hierarchical Forecast library, which contains different reconciliation algorithms, preprocessed datasets, evaluation metrics, and a compiled set of statistical baseline models. This Python-based framework aims to bridge the gap between statistical modeling and Machine Learning in the time series field.
ABOUT THE SPEAKER:
Max Mergenthaler is the CEO and Co-Founder of Nixtla, a time-series research and deployment startup. He is also a seasoned entrepreneur with a proven track record as the founder of multiple technology startups. With a decade of experience in the ML industry, he has extensive expertise in building and leading international data teams. Max has also made notable contributions to the Data Science field through his co-authorship of papers on forecasting algorithms and decision theory.
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[@Darokbr]

I am following this package. Very interesting.

[@TheKinsey06]

I face these exact issues at work. I needed this video

[@Alfalphe]

What about Exogenous variables?

[@FindMultiBagger]

Hirachical forecast is really slow , but hats of statsforecast developers and contributers for ecosystem 🎉 😇♥️

[@gauravsukhadia638]

Hypothetically if I have let's say 4 million skus what do you think how long will it take ?

[@FindMultiBagger]

@@gauravsukhadia638 For me their are 10 Million sku , for 1 million sku it took 3/4 hrs , but their are lots of issues like , historical validation and future forecast. but I able to fix those issue myself. suggestion use OLS method for faster execution MinT is slow

[@517127]

How acess the residual or rmse for each id?

[@Leibniz_28]

Great initiative