Manu Joseph is a self-made data scientist with 10+ years of cross-functional experience spanning Analytics, Software Engineering, and Supply Chain consulting. He has extensive experience working with many Fortune 100 companies in enabling digital and AI transformations in their processes, specifically in Machine Learning based Demand Forecasting. He has also been an active open-source contributor and developed and maintains an open-source library - PyTorch Tabular - which makes deep learning for tabular data easy and accessible.
Synopsis:
The Global Forecasting Model is a newer paradigm of Forecasting. He shares insights of a book on forecasting titled “Modern Time series forecasting with Python”.
Points covered:
Introduction of Time Series.
Machine Learning for Time Series.
Global Forecasting Models(GFMs)
Strategies for improving GFMs
Time series: A time series is a collection of well-defined data observations obtained through repeated measurements over time.
He covers the basics of the Time series and demonstrates the same with real-life examples. How can the Time series relate to Machine Learning? Will forecasting work with Artificial Intelligence?
