Time Series Talk : Autoregressive Model

[@madeehasayyed9507]

Its for the first time that I have seen someone explaining econometrics in such a simple but yet in a comprehensive manner. You are a life saver.

[@taghreedalghamdi6812]

I'm doing research and it's involve with some of the concepts you mentioned, I've never been felt how easy to understand these concepts till I saw your video!! Big Thanks to you ,, please keep posting more videos for the sack of science research and education.

[@AbdullahAfzalRaja]

is your research by any chance is on ARx model? doing the same :p

[@victorgaluppo5233]

Ritvik, you really have a gift for teaching complex topics in such simple terms. Seriously, I'd been trying to find an understandable lesson, and yours was godsent! Thank you very much for taking the time to help us!

[@thefuturAI]

So well explained again - you are brilliant at explaining the concepts in a way that's easy to understand - THANK YOU!

[@ritvikmath]

Glad it was helpful!

[@rjsmotel]

It is incredible how well you teach. These videos are fantastic, thank you

[@ritvikmath]

Glad you like them!

[@user-rh3ie8no9n]

you’re a lifesaver!!! the amount of light bulb moments I have in your videos is insane

[@lelewang6661]

this is the easiest but best video I saw to understand AR Model! thank you very very much!

[@ritvikmath]

Glad it helped!

[@Zeel_BTS]

I am absolutely amazed. Thank you so much for this

[@MrManumuna]

Bro, this was easily the best explanation I've ever heard so far. Thanks a lot!

[@vigneshrb1626]

Gem of a series for anyone studying about time series!!

[@hueyfreeman9504]

Oh my Lord!!!! This is amazing! They could pay people money from here to the moon and they wouldn't be able to explain this concept so concisely. Best explanation of AR Model I've heard. Thank you so so much!!

[@yerseitbalkhibayev9438]

It's amazingly simple and clear explanation of such a elusive topic! Thank you very much

[@baskaranthangarajan4443]

Really a gentle but a very powerful and intriguing intro to the AR model. Thank you.

[@apoorvmalik6122]

This is so helpful!! You cleared all my doubts. Thank you very much for making this.

[@ritvikmath]

Glad it was helpful!

[@ritukamnnit]

Thankyou so much, This video was of great help. one of the best material explaining time series forecasting. :)

[@arungautam3454]

Brilliant explanation. So easily explained this confusing topic.

[@syedbaryalay5849]

came here for copper, found gold instead. You doing a great job with these video my friend. thanks

[@VanessaHenderson-j2i]

Wow! You are a principality, with due respect this is mind blowing

[@TheExceptionalState]

Thank you so much for your clear and well put together videos

[@ritvikmath]

Not a problem :)

[@robertopizziol7459]

2020 hit us so hard no statistical model could hold. I bet even the milk demand is a total mess now!

[@anthonyng3705]

Most error in prediction models answers only how many % chance an event happen. BUT THEY NEVER ANSWER YOU the magnitude WHAT IF THE SMALL CHANCE HAPPEN. Some events like 2020 here rarely happened, but when breaking out, its magnitude swipe out everything. HAHA

[@sassmos008]

Although some model may not hold, this will help us factoring in the effects of such events when we deduce other similar models.

[@olivermohr417]

@@anthonyng3705 That's what you call Excpected Shortfall in finance. Expected loss given a tail event

[@mayurkagathara3601]

kzbin.info/www/bejne/pJ_aoqeQnr6ArrM . Case study on Amul during covid. Every hard hit comes with momentum that can destroy us or push hard to be the best of all time.

[@zacharyadams3772]

I’m a data scientist who worked through the pandemic in a critical infrastructure industry. On the other side now, can confirm, standard methods rendered results like 1+1=purple.

[@playkids5]

Taking your videos help in 2023🎉❤thak you ritvik or ritik sir

[@graceegan3005]

This video is amazing. Thankyou for explaining this so well

[@ngotrieulong6935]

So great sir, hope to see more video about time series from you, it is really benefits for me

[@szymonk.7237]

Thank you for this series ! ❤️❤️❤️

[@ritvikmath]

You are so welcome!

[@gooeyyeoog8535]

Great video man ! Big love from Saudi

[@christianbauer3417]

Amazing easy explanation my friend! It's a pity that you didn't explain the beta coefficients in detail, but I understood the concept very well :-) Thank you for your help.

[@asadkhanbb]

You made my intuition clear. Thank you

[@hu5116]

Hi: I love your videos! If you might be looking for some new time series topics, it would be great to have some focused on signal processing topics such as Yule-Walker, acorr, Linear prediction, and Burg, and also time series gap filling. There are probably at least half a dozen videos among these topics and I know a lot of EEs would be eager consumers of these topics. Thanks for all you do!

[@ritvikmath]

Thanks for the awesome suggestions!

[@cameronhashemi569]

Hi Ritvik, thank you for these viedos. It seems like this one should be the third one in the time series playlist, after ACF and PACF are introduced, but before the coding demo which already references AR.

[@Harikrishnan_1505]

Thanks a lot. You're undoubtedly a genius.

[@Alex-sy4gg]

well. correct me if im wrong. i dont think AR model can skip lags tho, meaning it needs to start from t-1 and follows in time order i believe

[@user-cc8kb]

Very nice explanation. Thank you a lot!

[@libo8318]

Wonderful explanation!!!!!! do you have video explaining the differences between AR-MA-ARMA-ARIMA?

[@manaoharsam4211]

Very good, well explained.

[@ritvikmath]

Glad it was helpful!

[@christosmantas4308]

Thank you, very nice explanation. Q: How do you draw the "error" lines (red dotted) in the ACF plot? What is this threshold for significance?

[@jairoalves8083]

Holy man, you are a natural!!! Thanks a lot!!!!

[@mohamedgaal5340]

Hi! The milk graph shows seasonality. I'm wondering how could you use AR model on a nonstationary time series. Thank you.

[@KIKI-NJ]

I have the same question

[@SuvodeepPyne]

That's what ARIMA model is for. He has a video on that.

[@shadrackdarku8613]

this stationary time series the mean is fairly constant

[@anelesiyotula5372]

Hello. If there is seanality you could just do a second difference to remove it.

[@janis.5733]

Thank you so much 😊

[@sorooshtoosi]

Thank you very much! it is a very well explained and useful video!

[@Coopy55]

Well explained. Thank you very much you may have saved my assignment haha

[@leonfan1394]

You are a great teacher

[@AviadAvraham]

amazingly simple explanation, thanks! My trouble so far is understanding what the beta coefficient(0) or intercept is. can you explain it briefly please?

[@yangzou8260]

Great tutorial! I thought the underlying requirement of AR model is the stationarity of the data, which this example violates. Could you explain why we could apply AR to this data of strong seasonality?

[@VKRealsta]

Really such a wonderful and understandable vedio this is.

[@pablouribe1522]

Excellent video!

[@brandre]

Thanks for this very clear explanation!!!

[@hanadibinmujalli965]

Thank you so much, brilliant!!

[@Rodrigo870]

Great explanation! Thank you very much!

[@BBB_025]

for the AR model you made for m(t), would this be an AR(4) model because there are 4 lags, or would it be an AR(12) model because the largest lag is 12 periods before the current time t?

[@phut7755]

I think in this case, the model would be considered an AR(12) model. Even though there are only 4 significant lags (1, 2, 3, and 12), the largest lag is 12 periods before the current time t. When specifying an autoregressive model, the order of the model is determined by the maximum lag included in the model, which in this case is 12. The AR(12) model would include all lags up to the 12th lag, with some coefficients possibly being zero or near-zero for the insignificant lags.

[@108987]

@@phut7755I would beg to differ. We denote an autoregressive model as AR(p), where p denotes the amount of lagged variables included in the model, which in the case of the example from this video is 4. Hence it is an AR(4) model.

[@terryliu3635]

Great video! Thank you very much!

[@bonadio60]

Hi, great videos! I am following the series and one thing that is not clear is that this milk chart seems to have a seasonality. My question is, if you can model it with just an AR model why do I need the "s"arima model? I will answer my own question, I think I understood. The SARIMA is just applying "AR" "I" and "MA" over the seasonal lag. So for example if I have an yearly 12months seasonal data using just AR(12) would calculate the regression over all steps/months 1,2,3,4,..12 but if I have S"AR"(12) it will just calculate the regression on the 12th lag

[@rishabstudies2822]

Great video! Just one thing I didn't completely understand. when trying to find the model of Mt, where do the beta values come from? Thanks! (timestamp: 7:18)

[@WahranRai]

before talking about AR model, the time series must be STATIONARY ! AR and MA models are based on stationary time series

[@sameer123wipro]

Brilliantly explained

[@kisholoymukherjee]

great video as always

[@Juan-Hdez]

Very useful. Thank you!

[@Silver1980love]

Great video, keep going.

[@mohammedghouse235]

The PACF appears similar to Tornado plot in uncertainty analysis.

[@alecvan7143]

great video!

[@ritvikmath]

Thanks!

[@pawankulkarni7634]

yes, Video is superb. How can we select order of AR model from PACF and same for MA model from ACF.

[@statisticianclub]

Great explanation

[@MrTony337]

In this example the data is seasonal, does this mean we need to make the data stationary before we use the PACF plot?

[@lazlopaul7764]

Thanks this is so informative!

[@tanvirkaisar7245]

hi @ritvikmath could you please upload a tutorial about Copulas?

[@ParneetKaur-tq6qy]

really very helpful

[@ritvikmath]

Glad you think so!

[@leg9004]

thanks a lot for your work

[@ritvikmath]

You are welcome!

[@azeturkmen]

thanks a lot, sir! helped me a lot, to understand concept

[@RachitVerma-f2k]

How do we estimate the variance of the white noise from the given data?

[@pbmann69420]

Hi sir, seeking for clarification here, why is it that AR Models can only be applied to stationary time series? This one here isn't stationary due to seasonality, but it seams like the seasonality helps in the prediction, due to the 12th month adding an additional month that helps predict the current month?

[@luigifiori4812]

great job sir!

[@michaelangelovideos]

This is amazing, thank you.

[@arunpalaniappan4749]

Hey Ritvik! I had a doubt, what is the difference between a simple exponential smoothing and an AR model? Simple exponential smoothing predicts the next value as a linear function of the previous values, but weighted. AR Model also predicts the next value as a function of the previous ones. So is exponential smoothing a subset of AR model or how does it go?

[@marvinalbert]

In exponential smoothing, the used weights follow an exponential model. In AR, by contrast, there's no constraint on these weights. So as you suggest, exponential smoothing in this context could be a special case of AR.

[@michaelelkin9542]

Later videos say that AR cannot be used on a seasonal model which this clearly is. But the model is based on the seasonality. So can it be used or not?

[@tiagocantalice9767]

Thanks for the lesson. Help me a lot. ;)

[@ericmcalley6097]

Excellent video. Clearly explained and loved the crayola markers. For this, would you use Level data or first differences? Thank you

[@prameelagorinta4626]

Hello sir, Won't the t-2, t-4 terms get negative sign, as they are in the negative direction?

[@TheOrionMusicNetwork]

The coefficient can be a negative value (e.g. b2 = -0.6). No need to use negative signs

[@fairuzi]

can we predict commodity prices based on weather?

[@DauphinetB]

I'm having a problem with the definition of order of AR, MA and ARMA time series forecasting processes. Imagine we have a time series with data from January to December, and we're in July, trying to predict August. When we say AR(2), are we using lags relating to July and June, or can those two months be any month between January and June?

[@anandmaurya6133]

I learned the info from your last video that to apply a time series, the data shouldn't be seasonal. Isn't the data here somewhat seasonal to apply a time series forecasting model ?

[@drmearajuddin2334]

What an amazing explanation sir.. Great sir.. Sir plz make video on cointegration especially Johensen cointegration.... What is difference between VAR AND AR.. PLZZZZ HOPE TO SEE YOUR REPLY

[@DM-py7pj]

8:29 where has the mean term gone? Looks like it is represented by beta sub 0.

[@shobhitsrivastava9112]

What will be the values of Beta0, Beta1 and so on? Is it same as the value of PACF?

[@chethan93]

Very good video!!

[@김주영-d1c]

Thank you for the video. From the video, I have two questions in mind, 1. Is AR model built from PACF? 2. Can we also build AR model from ACF? Hope to hear some from you!

[@statisticslearning]

AR model is identified or built by PACF plot And MA model is identified or built by ACF plot... Always remember

[@zhixu1925]

Great Video! My questions are: 1) In your first video about ACF and PACF, as long as there is a time series, i could plot ACF and PACF regardless on whether its stationary or not by my understanding. In this episode, the time series need to be stationary in order to implement AR model. Why is that? 2) In my case to analyze stock price, the first step is to plot ACF and PACF. Do I need to make stock pice stationary in order to perform ACF and PACF? Thank you !

[@zamiphilicknnox6720]

I maybe wrong but i think he was just checking the time series data for stationarity. Becuz if its stationary we go for OLS and if not stationary we try and apply ARDL model to the time series data.

[@fyaa23]

A nice introduction. Maybe you could use the example data and show the prediction curve to get a sense of the outcome.

[@chuckgrigsby9664]

It would seem to me that from your discussion of the use of the PACF to identify the important contributors that you have missed the lags at t-24 and at t-36 unless your analysis makes the assumption that the quantity has a periodicity of one year. But you didn't discuss periodicity in your approach to the PACF.

[@Freddyhb1]

But what is the Coefficient B?

[@gravimotion_Coding]

How do you calculate the red bands, so that you can check which lagged value has an impact on the model? thx for answer :)

[@user-or7ji5hv8y]

I wonder when is time series model not dependent upon its past series. Does it need to be called autoregressive, to indicate that?

[@airesearch2024]

please make more time series video! It really helps! and there is no much time series video out there at all

[@bermchasin]

me also like much time series video. Hope make more video for knowledge.

[@user-or7ji5hv8y]

Seems like AR is for capturing seasonality.

[@yasminedaly7648]

But aren't you supposed to stop at the first insignificant lag, in this example 2 lags were significant then lag 3 was not so a good model should be AR(2) and not AR(4) right ?

[@L.-..]

For this AR model what will be the p value? That is, AR(p) -> AR(4)? Is that correct?

[@muhammadmoazzamaliashraf4977]

ACF video link please

[@kosprov69]

@ritvikmath In the video for stationarity, you mentioned that we need stationarity to apply AR/MA models to the time series. Furthermore, for a time series to be stationary, it had to have the following criteria : 1. The mean / expected value remained constant 2. The variance remained constant 3. There was no seasonality Yet in this video, we are using an AR model to solve a problem which is completely seasonal. This felt contradictory to your stationarity video.

[@shivkrishnajaiswal8394]

is the data is stationanry, then shouldn't the PACF plot be periodic with period of 12?

[@irfanshaikh-ub9ks]

can you please explain with the help of raw data set

[@ritvikmath]

Please refer to this video: kzbin.info/www/bejne/gHSufolmeNqeZs0 Best, Ritvik

[@PF-vn4qz]

so how much data do you need to do an AR model and estimate coefficients with PACF?

[@swiftblade168]

Superb