What are Autoregressive (AR) Models

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Time to start talking about some of the most popular models in time series - ARIMA models. First things first, let's look at the AR piece - autoregressive models!
  1. Aric LaBarr
  2. #forecasting
  3. #timeseries
  4. #data science
  5. #data
  6. #autoregressive
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Комментарии
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Thanks Mr. LaBarr, I'm studying for my exam in time series and your videos are very helpful. Greetings from Italy!!!

pettirto
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Came here after being confused by my Lecturer,
Thank you very much for simplifying this!

enock_elk
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Great video. I’ve had a text book about time series that’s been gathering dust because I was afraid of all the symbols. This helps a lot

hugoagudo
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my statistics is very basic and i just needed a forecasting algorithm, this video explained it sooo well

oren
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Thank you, j had seen this equation when a was studying reinforcement learning, it's like the Value function weighted by a discount factor.... Great explanation!!!

williamgomez
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One of the best teachers i’ve ever seen!
Thank you

oq
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just become my lecturer lol. i love the enthusiasm you put in. makes learning more fun lol

clickbaitpolice
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wow your teaching style is really amazing !! please make more videos on time series analysis. we really need your help!!

economicsfriendly
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I like the way you convey the intuition behind AR and MA models. One thing that might be confusing is however the terminology, in particular with regard to short and long memory, which is different in common literature. Therein, AR, MA and ARMA models are considered to be short-memory models, because their autocovariances are summable. Also AR models, whose autocovariance function (ACVF) decays quite quickly towards zero for increasing lags, even though the ACVF values in fact never fully reach zero, has summable autocovariances. In contrast long-memory behavior is indicated by a hyperbolically decaying ACVF, which results in an ACVF whose elements are not summable anymore. A popular example is the fractionally integrated ARMA model, often denoted by either FARIMA or ARFIMA, that can still have ACVF values of notable magnitude for large lags.

arnonym
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God bless you for your efforts to explain!

vadimkorontsevich
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Excellent teaching! Thanks for your good work Aric!

felipedaraujo_
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Hi Dr Aric LaBarr you work is Amazing please continue this again

Under 5 minute concept is great

ahsanshabbir
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Excellent contribution, thank you very much

rossijuan
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simple and beautifully explained! thanks!

josealeman
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man, you are incredible!
Im learning ARIMA like im building legos!

valdompinga
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A lot of overlap here with an infinite impulse response filter from DSP. Im about to watch the moving average model video, but am wondering if that is the finite impulse response equivalent :)

Atrix
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Hi Aric!
This was such a splendidly explained video. I have a doubt though about NARX. Do they function the same way as this one (explained in the video) because NARX is also autoregressive model? If not, could you please explain about NARX as well?

NishaSingh-qfit
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Nice video. Will you be making something about the ARCH/GARCH model :-)

dineafkir
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Can you explain the difference between Static, Dynamic and Autoregressive Probit models?

dipenmodi
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Hi Aric, thanks for the explanatory video. Can it be said that AR(1) is equivalent to Single Exponential Smoothing algorithm because it too depends on the Previous forecast and error.

kumaratuliitd