InfoQ
Forecasts are critical in many fields, including finance, manufacturing, and meteorology. At Uber, probabilistic time series forecasting is essential for marketplace optimization, accurate hardware capacity predictions, marketing spend allocations, and real-time system outage detection across millions of metrics.
In this talk, Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered.
This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl
If you are a software engineer that wants to learn more about machine learning check our dedicated introductory guide https://bit.ly/2HPyuzY .
For more awesome presentations on innovator and early adopter, topics check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz
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What was the book the lecturer recommanded at last, plz? I found it hard to figure out the author's name…
Waste of time, there are other videos better than this. No substance.
Here is also the document referenced in one of the slides which provide a lot of detail behind their architecture. https://arxiv.org/pdf/1709.01907.pdf