Damn! this guy is good.

lol

@@iLLt0m it is really useful :D

On behalf of all of us who have tried to use FFTs to forecast the stock market and had a rude awakening: this is spot on.

@@rushthedj306 there is no periodicity of companies prices, so I dont see the point...

@@piotr780 yes that's the point

@Aden Jett why would you post that here in a discussion thread about FFTs and misguided attempts at market prediction?

Fourier just extends the past, and nothing fits. Actually no algorithm i have used fitted, and people go wild if a single tick actually matches. I give up on predicting futures 😆

@@LawrenceReitan I have not used TensorFlow, so I do not know. When I do AI/ML stuff, I tend to write everything myself.

@@k98killer 💀

Stupid quote.

I came in here 2 years after I like my own comment without noticing it was mine 😅

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Not code, but Reducible has an excellent video breaking down FFT in detail.

You too man

100% agree

maybe this is errors after removing high freq noise? +1 to the question

Hm, seems like he applied a threshhold to his errors, i.e., error0 = fitted - original; strange_error = {0, when abs(error0) =thr}

i think it cannot

not in the additive way you saw in FFT. But if you implement attention in the encoder, you could interpret attentions weights similar to Auto Regression weights

Same way they decompose them

@@zhangshaojie9790 After a bit of reasoning I reached the same conclusion. The method works very well in particular for reducing the maximum forecasting error in a time series. I found that Facebook Prophet does also a good job in reducing the maximum error.

This lecture series on recurrent neural nets by Andrew Ng should help. It explains encoder and decoder, and many other aspects of recurrent neural net architectures. The lectures assume that you already understand the basics of neural nets (a standard feed-forward neural network). kzfaq.info/sun/PLkDaE6sCZn6F6wUI9tvS_Gw1vaFAx6rd6

It always will be. There is nothing more ideal than an algorithm that can teach itself through trial and error, what its supposed to do. The more precise you apply the algorithm, the better the results. I.e. using seq2seq for language comprehension.

ikr lol

Yea, you are right. This is because some trend market are often manipulated by market makers and such trends usually falls outside the 2 standard deviation move of price. I am glad that he talked about it. I see that his idea might work in a range bound market but not in a trend market.

No markov decision processes are pretty simplistic and don't utilize all the sequence data to calculate the probabilities

that decomposition called multiplicative classical time series analysis, it's pretty simple, not too complicated.

Maybe he is working with data that they can casually infer is periodic or seasonal trend data unlike perhaps the FOREX or stocks? - I think he states when to use it @6:02

Why wouldn’t it be?

Yes, it predicts an infinite repetition of the input by definition. That's why you have to remove mean and tend before application. Better to use this technique to remove noise, and then forecast by other means.

Exactly

@@ColocasiaCorm Because it would violate stationary.

I am trying to understand what L1-integrable means, and in this context are you trying to say that there is no trend?

@@mitalpattni1977 There is no trend but that is not what I mean. I'm trying to say that he has no idea that he is not allowed to use Fourier analysis for this type of data, because the data is not fulfilling the basic formal conditions required for Fourier analysis, such as L1-integrability (I hope this is the word, English is not my native language :) ). It looks like he has no education in calculus, signal theory or forecasting, and he is excited about the first data analysis tutorial that he found on youtube, but he doesn't even know that methods cannot be used freely without concern, you have to follow the rules.

@@jacekwodecki3530 hey jacek I believe you vocab is correct I just googled it, but I couldn't find a good explanation except for some definitions of L1 - integrable. It would be great if you could share some material on it.

@@mitalpattni1977 You say that you have found definitions of L1-integrability, so what is that you need beyond that? Happy to help.

@@jacekwodecki3530 what I meant to say was some legitimate article on it instead of stack exchange answers.

Are you saying that the various types of neural nets aren’t getting the job done as well as the old algorithm, or are you saying that they are inefficient in terms of man-hours (debugging and tuning) and computation? If the case is the former, then we should revive the old techniques, if the latter, then it’s completely sensible to keep the trend moving. Apart from nostalgia, I can’t say the abacus is the better application than the calculator on my phone or that paper-books are better for my learning than e-books on my G-Drive. Technology will keep on progressing, and we humans must adapt. If ARMA and GARCH must now live in museums, so be it! We who have had opportunities to work with them can always go visit on our own time.

There are still some definite advantages to the "old" style of model building. With deep learning, all you get is the result. Using techniques like ARIMA, GARCH, and partial differencing, you can get a lot more insight into the behavior of the data. You know what kind of AR and MA models you have, what that implies about the source, etc. With deep learning though, all you get is the point forecast and maybe an interval. It might be better for forecasting with a stable information set, but to be able to predict the impact of outside factors using explicit weighted coefficients and models is a lot more helpful for less informationally stable data sets.

i'm saying both of them......... for the computational time, i have no doubt that you gave it a try....... for the accuracy, i try things when i have got time.... i have no idea of the experience you have in the field of forecasting things in the ' non academical' field........ when i taught at the university, the first thing i said was what i was about to show was on data ' calibrated' for students to understand... when you show data in the real life ( retailers, to name things) , things are differents ........ erp and aps integrate ' forecasting tools' as welll...... they always give a result..... mmmmm is it useful? well... it depends....... most of the time when you want a solution, you have to build it on your own, step by step....... just for the fun i tried an lstm model on the tuberculosis data in china, which is a dataset you will find on the web..... make the comparison with a model from the sixties (!!!!!), and we will probably discuss some more about nostalgia! :-).......... eventhough when the lstm is properly calibrated, the old man runs faster and longer! ( ok, not much...)......... last but not least, i hope you have the mathematical background.... so you know that AI often reinvents the wheel, and gives anew name to existing things ( ok ok, i was not talking about cnn) never forget that the use of genetic algorithms, tabu search, or simulated annealing was supposed to dramatically improve things...... well..... cheers mate

definitely.......... most of the people forget that ann are blacboxes.......... when they work, it's perfect....... in my professionnel life, i never met anything perfect ( well, in more than 20 years, i had the time!)........ with an ann, trying to see were the problems are is impossible, the only thing you can do is change your model ( ann, cnn, kohonen, counterpropagated, and so on) or change the parameters ( learning rate, noise, number of layers, hidden units...) with old models, you can see step by step what's happening..... last but not least, in the real life, most of the time, when you want to fix a problem, you try several kind of tools.... a tool suitable for a probelm will not work for another one, which seems to be similar.......... you can't do that with neural networks... as an example, let's point out what you said.... your time series is not stable in it's parameters ( i'm not talking of integrated modelling...) tests will tell you there is something ' to monitor'........ nothing like this with an ann.... as new data are forecasted you can just see that the more you forecast, the bigger the error..... and you ' retrain' it ' to learn' ( well well well !!!) cheers

Jacob Swe Great insight. What learning materials (books, courses) will you recommend to develop the understanding of time series methods?

Nice :)

Aren't GAM's linear models? seq2seq models are non-linear.

@@stefanosgiampanis4898 no, they can be non-linear. If they were only linear wouldn't bother to comment at the video.

@@safishajjouz166 Can you point me to a reference about how a GAM can be non-linear? en.wikipedia.org/wiki/Generalized_additive_model

@@stefanosgiampanis4898 the well known book by Hastie. Otherwise, in a GAM model use f(X) = a+bx+ b^2x^2 ... As a very simple example of a nonlinear model

@@safishajjouz166 That makes sense, thank you. The non-linear f(X) is, however, specified (not entirely learned as in the case of a NN). Not criticizing GAMs or arguing in favor of NNs.

which is?

Attention is all you need

бест опинион