Your explanation and summary is much better and cleaner than my professor’s two-hour long lecture, much appreciated!

how do i like this more than once......thanks man

i love how you are explaining this topic with real world examples.

Bro i am indian nobody teached us these topics on any platform thanks i am watching your videos❤❤❤

Bro is doing God's work in Crayola

Your really good at explaining difficult things, thank you!

You have the best TS course on KZfaq! THANK YOU SO MUCH!

Thanks for the explanation!! Better then a lot of university lecturers!!

hey ritvikmath i have a forecasting final tomorrow and its 2AM rn and im binge watching all ur videos.....i love u....love from Toronto Canada

Thank you a lot for helping me understand this well.. I plan to see this entire series, its really well explained & in simpler terms. I wish you were my professor. Thanks again!

Thanks!! With the pandemic, my time series analysis classes are getting very complicated, but here I am getting a good understanding of the ARIMA model. Thank you !!

One of the most simple and concise explanation of ARMA model!!

oh my god , after lot of videos this is the clear explanation,

wow, you broke this down so nicely. Thank you.

You made the things easy peasy for me. Thank you....

Your presentations are as clear as fine water. Thanks a lot for your help. Gongratulations. Would you mind presenting more videos in econometrcs models GLS models and more advanced.

Great video and great explanation!

I really love this, thank you

Thank you for your explanation!

Thank you!

You are my hero Thank-you !!

One thing that I didnt quite understand: Does the Order describe A.: HOW FAR you can look back (e.g. to the t-Pth value) or B.: HOW MANY TIMES you can look back (so e.g. Order 3 means there are 3 lags in the ACF/PACF that are different from 0)

Excellent explanation!! However, one note: I think the language you are using to describe the epsilon is not quite correct. In particular, in your MA model video (which is also excellent) you describe the epsilons as a white noise process but here you describe them as deviations from our previous estimation. I believe they are a white noise process (as you said in the other video) and not deviations from our estimate (since that estimation does not exist yet). Please, correct me if I am wrong.

You will never replace Ben! But, decent examples lad

thank you ❤❤❤❤❤❤❤❤ u'r life saver

I'd be very interested in how the regression of such a model is made. Probably not that crazy, but I am a little startled because the errors would probably be dependent on the coefficients.

Thank you so much!

Thanks for the video. What if the PACF show sig for 1 and 4, but not 2 and 3? What order should we give to AR?

Nicce!

Thanks 🙏

At the 6:00 minute, if the 2 ACF spikes are at interval 1 and 3, would the ARMA still be (1,2)? Are the input based on the number of spikes above the red dotted lines?

Hi Ritvik, excellent video. Can we infer that AR part behaves like mu of MA (as you mentioned in previous vid) to get the baseline for which we want to smooth the errors ??

nicely explained ! if you add same with real data on excel and then explain ARMA(1,1) it will be amazing !

"a time series in the wild" gets me every time

This is a basic question on Box-Jenkins MA models. As I understand, an MA model is basically a linear regression of time-series values Y against previous error terms et,...,et−n. That is, the observation Y is first regressed against its previous values Yt−1,...,Yt−n and then one or more Y−Y^ values are used as the error terms for the MA model. But how are the error terms calculated in an ARIMA(0, 0, 2) model? If the MA model is used without an autoregressive part and thus no estimated value, how can I possibly have an error term?

great

I have a question, for a time series to make use of ARMA model, the time series has to be stationary right? If it is stationary, It means it fulfill the requirement of there is no correlations between current t to any previous time which means there would be near 0 for ACF. Then there wouldn't be any instant that it would be higher than the blue dot line right? Or am I missing something?

Last video you talked about the invertability, so based on that, ARMA(1, 1) is equivalent to ARMA(infinity, infinity)?

Really good one

Question - is the L t-1 (the AR part of model) should be what I predicted for last period or what was the actual demand at last period??

The only thing I cannot understand: Why are there only error terms in the MA part of the model, where is the actual moving average? (as given in your previous video on the MA model, as μ). Do we assume it to be = 0? Thank you.

Nice explanation. But what about pre-requisites for ARMA like stationary , removal of trend and seasonality etc ?

dude you are fucking awesome!

When forecasting values, does value of e_(t-1) remains constant, if not how do we determine its value?

good one, thanks

Thanks!

I fuckin love watching signal processing while high

Question about that last part of the video: 1) Are you running the ACF and PACF on the observed data or on the residuals data? 2) If my PACF shows a spike at 12 (eg: a certain month of the year has seasonally high demand), do i then set ARMA(12,1)?

In the example, if a good model, according to the given ACF & PACF, would be an ARMA(1,2), so, there is missing a term such as "phi_2 x Epsilon_(t-2)", ¿right?

I think you swapped meaning of acf and pacf?

Dude why the MA() order is 2 but not 1? What singles out 2?

Any way to calculate the suitable error threshold for ACF/PACF plots?

For the MA(1) part why didn't you include mu value to calculate lsubt?

is there any way to use ARMA((1,3), 1) processing in R?

You are a G

I think I once saw a time series in the wild. But I am not sure... I am not good at math and can't understand anything here... why am I here ...

University lecturers need to dissect ARIMA to AR and MA before diving to ARMA and ARIMA.

1:40 the clue

Thank you!