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Introduction to Forecasting in Machine Learning and Deep Learning financial deepmind

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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:

If you are a software engineer that wants to learn more about machine learning check our dedicated introductory guide .

For more awesome presentations on innovator and early adopter, topics check InfoQ’s selection of talks from conferences worldwide .

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Introduction to Forecasting in Machine Learning and Deep Learning

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Introduction to Forecasting in Machine Learning and Deep Learning
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17 comments

Frederico Bianco 22/09/2021 - 9:31 Sáng

what is the name of the book that she says in the end? 11:15

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Goon Millet 22/09/2021 - 9:31 Sáng

.

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Mehrdad D 22/09/2021 - 9:31 Sáng

Short but full of learning ideas for me! Appreciate it.

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karam ali 22/09/2021 - 9:31 Sáng

Gives you wellness, Professor ♥
Please help .. I need data set .. to train Model to predict the occurrence of fires due to the large number of fires that happen today in Syria (more than 100 fires)
Please Help

Reply
Crossbow 22/09/2021 - 9:31 Sáng

This video was a complete waste of time. Nothing of value was imparted.

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Peter Heine 22/09/2021 - 9:31 Sáng

Hello Sir,

I have a question about forecasting. Should plan corrections be determined during forecasting? Or is it not necessary to determine them? Why is there an extrapolation? An extrapolation is absolutely necessary.

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sgrouge 22/09/2021 - 9:31 Sáng

just a commercial presentation. Lost of time.

Reply
Niccolo Tosi 22/09/2021 - 9:31 Sáng

Excellent delivery

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Chris Drake 22/09/2021 - 9:31 Sáng

There are some time-travelling mistakes shown here. "passes" are being run https://youtu.be/bn8rVBuIcFg?t=315 – that's the mistake. Pass 2 has been affected by pass 1 (double bad there's even overlap in the test). In other words, pass-2 has the benefit of knowledge from the future. Here is where the mistake manifests: https://youtu.be/bn8rVBuIcFg?t=619 – that is plotting the equivalent of "pass 5", which has had multiple generations of insight into the future. If an entirely new and never-before-seen test set had been chosen, and had been run and shown FOR THE FIRST TIME EVER during this talk, then the output is meaningful. Right now, it's just fools-gold. The "FIRST TIME EVER" statement is very important. If it was run before walking on stage, and it didn't work, it would not get shown to us… yet ANOTHER time-traveller mistake. This talk is not useful to say whether or not it's superior to the other methods shown for solving the problem (two reasons: the time-traveller mistake in the ML, and the lack of giving the other models similar insight, or at least an even playing field). It also lacks comparison against any non-ML bespoke solution built by a statistician. If it's doing worse than a human-built design made by a team spending the same number of hours on the problem, then it's a step backwards, right?

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C 22/09/2021 - 9:31 Sáng

Nice presentation!

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C 22/09/2021 - 9:31 Sáng

Did anyone have success in finding the open source book by Rob Heinemann? Thanks

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sneha priya 22/09/2021 - 9:31 Sáng

The machine learning holds the highest CAGR of 44.86% during the forecast period 2019-2025.

Request a sample @ https://www.envisioninteligence.com/industry-report/global-machine-learning-market/?utm_source=yt-chitti

Reply
3D Printwiz 22/09/2021 - 9:31 Sáng

Can you use this model in predicting stock prices?

Reply
sneha priya 22/09/2021 - 9:31 Sáng

The machine learning holds the highest CAGR of 44.86% during the forecast period 2019-2025.

Request a sample @ https://www.envisioninteligence.com/industry-report/global-machine-learning-market/?utm_source=yt-chitti

Reply
Marcos Silva 22/09/2021 - 9:31 Sáng

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

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Raymond 22/09/2021 - 9:31 Sáng

Waste of time, there are other videos better than this. No substance.

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黄忠廉 22/09/2021 - 9:31 Sáng

What was the book the lecturer recommanded at last, plz? I found it hard to figure out the author's name…

Reply

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