Two Effective Algorithms for Time Series Forecasting

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Название	:	Two Effective Algorithms for Time Series Forecasting
Продолжительность	:	14.20
Дата публикации	:
Просмотров	:	331 rb

THESE differences, not THIS differences
Comment from : Aclama Greentea

Linear regression
Comment from : hans T

That's why it doesn't work in trading
Comment from : Daniel Sckarin

Amazing!
Comment from : Rajavel KS

This is extremely good example, however i would like to see one which there more irregularities, it may have about 1-2 year long periodicity and we may not have sufficient data etc brbrActually I came across such problem recently Time series decomposition had very large values in the error part
Comment from : Pinakin Chaudhari

The question remains: Why can't it beat the stock market?
Comment from : W Tan

Couldn't a wavelet transform be used instead of a FFT to catch high frequency signals?
Comment from : Mahad Mohamed

is there a playlist for all related videos
Comment from : Azaz Ahmed

I fell like this is way too simplistic for modeling financial time series data Under extreme financial stress and boom regimes, memory is both very short-lived and extremely long-live ie a function of subsequence proximity to said regimes (stress or boom)
Comment from : axe863

It might work somewhat in a sideways market but otherwise time series data is non periodic
Comment from : Alexander ONeill

How about transformer?
Comment from : isokaytoloveyousomuchabitmorelessmaybe

This is very well explained!
Comment from : Shan Dou

This is outdated even at the time of release seq2seq is no longer the best sequence model Not easy to train, not accurate enough result
Comment from : Yilei

I'm not understanding anything I'm new to this field
Comment from : thereal_HK

Of you master this you'll have the edge
Comment from : H A

Overfitting isn’t forecastingit’s not necessary and useless
Comment from : C X

Perfect presentation Sr ! I"m very interested to learn more about Can you indicate a literature or a code to do what you told on 4:21, the outages ? How can be done to compensate the mentioned problem on 4:21 ? I'm trying to figure out how to code itbrbrWith all respectbrRobson
Comment from : Robson

Excellent presentation
Comment from : J Flow

Wonderful presentation, very clear, very precise Thank you!
Comment from : -

Assalamualaikum brothers and sis, I am seeking Software Engineers, whom can code in Tensor flow and RNN Time series I am paying I am based in USA Please check out lighttheorypage/ or email us at info@LightTheorytech SALAM
Comment from : Light Theory LLC

Would be great to have some code examples for FFT or seq2seq Much appreciated, if someone can provide them!
Comment from : Noname Noname

Using FFT for forecasting the future: that's just like repeating the past with extra steps
Comment from : Jordan Miller

He forgot the most effective and fastest algorithm for forecasting tabular data
Comment from : Ed Powers

Sucks being new to this stuff lol
Comment from : teebone 21

This is true Gold!! thx :)))
Comment from : Yazmin Abat Alarcon

I recommend this
Comment from : Ipeleng Khule

Awesome stuff! Just in case anyone needs an app: @t
Comment from : Ole Ersoy

thank you, very well explained
Comment from : usbhakn

Outstanding presentation Thank you
Comment from : Eben Daggett

Great lecture!
Comment from : D B

I'm glad I actually watched This is AMAZING
Comment from : TheDawningEclipse

You deserve the like bro
Comment from : תמיר פריינטה

Please help in prediction time circle in STOCK MARKET
Comment from : SAMEER

Great video!!
Comment from : Victor Silva

Well that’s funny Almost everything advanced(or seems to be advanced) belongs to ‘deep learning’ In my opinion, this is just the state space model or hidden Markov models, isn’t it?
Comment from : Lincoln Guo

Time wasted
Comment from : Lei

I like this very much Short and packed with actionable information Thank you!
Comment from : Sriram Srinivasan

1 To perform Fourier analysis on a dataset it has to be L1-integrable In the presented example, the time series is not L1-integrable This method is good for 1st-year students, not for serious people In such an example you should use proper modelsbr2 Did he just hugely overcomplicate the idea of autoregressive modeling?
Comment from : Jacek Wodecki

hahaha, tao biet ma, bon may an cap cua tao nhung cung deo co ra hon cai gi het :D tuc toi lam ha lu cho heo ga zit que, tiep tuc di, lam tiep di, tao cho ket qua bon may do lu phat xit cho, hi hi
Comment from : Greg Makov

I learned so much in 14min! Thank you for sharing your knowledge and experience!
Comment from : Nicolas Berney

excellent!
Comment from : Farid Abu Bakr

Thank you for the good talk
Comment from : shrvsmb gnchsh

damn this is shit
Comment from : SillieWous

Can someone provide the name of the paper from which the very last bit was taken? (The prediction with the encoder-decoder NN)
Comment from : Andreas Hadjiantonis

5:14 bris bottom line the error? between the red and black curves?brit seems the error varies along time, but why the bottom line looks almost horizontal?
Comment from : Ryan Ptt

Wow!
Comment from : Dadi Superman

RNNs are dead
Comment from : Anton Krasnokutskiy

I cant explain it like this This guy trully explains it Thanks for awesome video
Comment from : Bilguun Byambajav

If you can't explain it in simple words you didn't understand it This guy nails it perfectly that even my kid would get it
Comment from : Beibit-DS

These methods are interesting however they over complicate the forecasting process A simple SARIMA model would do the trick, maybe even a Holt's Winter seasonal model If u want to utilize Fourier terms a dynamic harmonic regression or sinusoidal regression might have been better
Comment from : Mario

wow this is stupid
Comment from : stkristiano

Can we decompose the time series using Seq2Seq?
Comment from : Prateek Jain

好不容易找了几个有效的例子实际中没啥用啊
Comment from : arthur zhou

learn statistics and stochastic processes, at least
Comment from : Joao Pedro

Very good tutorial Thank you for sharing!
Comment from : maiarob2

so the solution is deep learning again
Comment from : edansw

awesome vid! thank you for posting
Comment from : n z

FFT only for periodic function, furthermore, nobody can predict the unpredictable, Forex is bullshit, only them, the bankers may do it, because they are the cheaters
Comment from : Tobias Majoy

love it!
Comment from : Raúl RLS

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 ?
Comment from : bhumika lamba

I am sorry my friend , using a fft for forecasting is methodological nonsensebrDe implicit assumption of a fft is thatbr the timeseries is periodicbr Why would it be?
Comment from : Frans Mulder

1:14 Decompositionbr3:49 FFTbr14:19 Seq2seq
Comment from : Xiaobo Fu

Thanks for the talk Mind opening
Comment from : John Hammer

very nice!
Comment from : Ming C

Got it, Thank you very much!brbr:)
Comment from : Hafidz Jazuli

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 onbrsad really sadbri 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
Comment from : uncle max

it's very funny to see people with only bacholor's degree talking about data analysisbrFFT and RNN LMAObrI guess what's why uber sucksbrIt's 2020 now, use google scholar to read at least 100 top-tier papers before you start talkbrTo uber, please hire some real researchers!
Comment from : lisa s