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!
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!!
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.
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.
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
ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-nnwqtZiYMxQ.html . 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.
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.
amazingly simple explanation, thanks! My trouble so far is understanding what the beta coefficient(0) or intercept is. can you explain it briefly please?
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
Thank you so much for your video - I am actually watching your whole TS playlist and it helps me so much!! I have just one little question regarding the model you presented us with at the end: Shouldn't it be minus ß2 and minus ß4 as mt-2 and mt-4 have a negative direct influence on mt, which is then expressed in their coefficients? Would be great if you or anybody else could help me out. Thanks! :)
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?
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.
@@phutschinski_7755I 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.
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 !
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.
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?
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.
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
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!
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?
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?
