InfoQ
In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting.
Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform.
This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl
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it's very funny to see people with only bacholor's degree talking about data analysis…
FFT and RNN… LMAO…
I guess what's why uber sucks…
It's 2020 now, use google scholar to read at least 100 top-tier papers before you start talk.
To uber, please hire some real researchers!
when i was young, we were supposed to learn, arima arfima, arch, armax, state space filter, and all these tools useful for time series………. nowadays, no need for any skill, just do deeplearning, use tensorflow and/or lstm, and all the problems will be fixed ( whatever the problem, supplychain, wheather, finacial forecasting, …)…….. and that's the same for multidimensional analysis, econometrics, and so on……..
sad………. really sad
i just made a comparison between a state space model, and a lstm… no need to say who was the winner, who gave a result nearly immediately, who did not need coding and debugging too much, who….
Got it, Thank you very much!
🙂
very nice!
Thanks for the talk. Mind opening.
Good stuff
Good evening my Professor,
Please sir, if we have the Yt series. To study the stationarity of
this series, we can do the following decomposition (or filtering):
Yt=F(t)+Ut, such that F(t) is a continuous function according to the
trend (linen, nonlinear). And if we find the series Ut it is stationary,
it implies that Yt is stationary, and the opposite is right?
B.w
1:14 Decomposition
3:49 FFT
14:19 Seq2seq
I am sorry my friend , using a fft for forecasting is methodological nonsense
De implicit assumption of a fft is that
the timeseries is periodic
Why would it be?
Does anybody understand the part between 13:20– 13:58 . Don't really understand how encoder decoder things works. How exactly does the historical data in the encoder can be used in the decoder ?