Very helpful. I'm working through and online course on time series and you playlist is going to be an excellent supplement to the course content. Thank you!!
great video! just wondering why we dont need to input "miu" in the MA1 model, which was shown in the "Time Series Talk：Moving average Model" video? Thanks!
I was struggling with the same here and I think it would be great if this was explained in the video. I think for a matter of simplicity, they just considered mu = 0.
Let's imagine you have a toy car that you play with daily. How you play with the car one day might affect how you play with it the next day. Now, imagine if we wanted to predict how you'll play with the car tomorrow based on how you played with it in the past. An AR(∞) model is like trying to predict how you'll play with the car tomorrow by looking at every single way you played with it in the past, even going back forever! But that's impossible because we can't remember or keep track of how you've played with the car since birth. So, it's like having too much information to deal with. On the other hand, an MA(1) model is more straightforward. It only looks at how you played with the car yesterday and uses that to guess how you might play with it tomorrow. It's like saying, "Hey, since you played with the car this way yesterday, you might play with it in a similar way tomorrow." It's easier to work with because it only focuses on the most recent way you played with the car, not all the ways from the past.
Thanks for the videos. I am really enjoying studying these concepts from your playlists. Just a short comment. From MA1 model, C_1 = - phi * e_0 + e_1, so, e_2 = C_2 + phi * e_1 = C_2 + phi * C_1 + phi^2 * e_0. Propagation gives e_n = C_n + phi * C_n-1 + phi^2 * C_n-2 * phi^3 * C_n-3 + ... + phi^n * e_0. When n is large enough and phi < 1, the last term goes to 0 and Cn is expressed as the sum of the past C_n-k series.
Tysm for the helpfull vids! I have question, in your Lag Operator video, you rewrite the ARMA in terms of lag operator by (phi1Lyt + phi2L^2yt+...+phi3kL^kyt), but in this video you square the phi's as well. why is it different? thnx in advance, Greetings
I think it can be much more intuitively illustrated by simply changing the subject of the equation. Instead of Ct = ... you formalize it as EPSt = ..., and recursively plug in the corresponding formula.
Thank you so much for the video! I am rewatching it because I indeed have trouble understanding this topic :D I have a question: Why do you use the coefficient Phi for the MA-process? In my textbook, we use this letter for the AR-process, and for the MA-process we use the letter Theta. Or is that not so important?
You're right, but it is just notation. I'm actually facing this problem because every book or video I read/watch has a different notation, slowing the learning process.
This was illuminating (and fun!) You are a *great* teacher!
Thanks!
This is in fact a beautiful use of the operator theory, thank you for the video
Very helpful. I'm working through and online course on time series and you playlist is going to be an excellent supplement to the course content. Thank you!!
YOU HAVE SAVED MY DEGREE THANK YOU FOR YOUR VIDEOS ON TIME SERIES ANALYSIS
great video! just wondering why we dont need to input "miu" in the MA1 model, which was shown in the "Time Series Talk：Moving average Model" video? Thanks!
I was struggling with the same here and I think it would be great if this was explained in the video. I think for a matter of simplicity, they just considered mu = 0.
your videos are amazing!!! THANK YOU SO MUCH!!
1:50 can you use phi and theta interchangeably when referring to an MA process? In other videos you used theta only for MA
He is too innovative, I watched every video more than once
I see a recommendation for his channel and that too on Time Series, I click PLAY!
The causal diagram was just too good!
It's so cool..... I was able to guess in the end it was AR model before you said.... How ur videos are relatable ... awesome
thanks!
Let's imagine you have a toy car that you play with daily. How you play with the car one day might affect how you play with it the next day. Now, imagine if we wanted to predict how you'll play with the car tomorrow based on how you played with it in the past.
An AR(∞) model is like trying to predict how you'll play with the car tomorrow by looking at every single way you played with it in the past, even going back forever! But that's impossible because we can't remember or keep track of how you've played with the car since birth. So, it's like having too much information to deal with.
On the other hand, an MA(1) model is more straightforward. It only looks at how you played with the car yesterday and uses that to guess how you might play with it tomorrow. It's like saying, "Hey, since you played with the car this way yesterday, you might play with it in a similar way tomorrow." It's easier to work with because it only focuses on the most recent way you played with the car, not all the ways from the past.
thank you very much, I love your playlist on time series. wonderful explanations!!!
thank you my friend, you're the best
Thanks for the videos. I am really enjoying studying these concepts from your playlists.
Just a short comment. From MA1 model, C_1 = - phi * e_0 + e_1, so, e_2 = C_2 + phi * e_1 = C_2 + phi * C_1 + phi^2 * e_0. Propagation gives e_n = C_n + phi * C_n-1 + phi^2 * C_n-2 * phi^3 * C_n-3 + ... + phi^n * e_0. When n is large enough and phi < 1, the last term goes to 0 and Cn is expressed as the sum of the past C_n-k series.
What doesn't work out for me is saying that eps_t is a function of C_t, because eps_t is supposed to be white noise right?
please keep it up, I wish you'd re-organized the playlist for us to follow
Sir plzz make a detailed video on cointegration.. Especially Johensen cointegration...
@ritvikmath Just a question: how do we prove that the absolute value of Phi is less than one? or is this given?
Outstanding!!
What a great explanation! Congrats
Hi, there! I assist the students of a Time Series Econometrics course in college. Found this video while preparing a revision lesson. Pretty good!
@rivikmath that was sooooo clear. thank you!
I love your videos they are really helpful. Thank you so much
Thank you for your great video!
My pleasure!
I really miss your old video format with the white board only. Can I ask why you changed it?
wonderful job!!!!!!!omg i love you
Great presentation!
It would be nice to have at the end of each video a homework data set and a list of two or three questions.
The
best explanation ever! Thanks
you are the best of the best
So we are sayin because of invertibility we don't have to figure out error terms and use lagged value of actual time series itself. Brilliant!
Tysm for the helpfull vids! I have question, in your Lag Operator video, you rewrite the ARMA in terms of lag operator by (phi1Lyt + phi2L^2yt+...+phi3kL^kyt), but in this video you square the phi's as well. why is it different? thnx in advance, Greetings
Brilliant!
The arrow in the left diagram is more like a "function of", instead of "caused by".
Excellent explanation! Thank you!
Can you help around the logic of why ma(1) processes donot follow Markov property
Hi Ritvikmath, I was wondering if you give tutoring lessons in Mathematics for Data Science?
Thank you very much!
You're welcome!
I think it can be much more intuitively illustrated by simply changing the subject of the equation. Instead of Ct = ... you formalize it as EPSt = ..., and recursively plug in the corresponding formula.
briliant vid !!!
Why have u omitted the mean in the MA(1) model?
omg thank you for making this make sense to me
Pure genius
Thank you so much for the video! I am rewatching it because I indeed have trouble understanding this topic :D I have a question: Why do you use the coefficient Phi for the MA-process? In my textbook, we use this letter for the AR-process, and for the MA-process we use the letter Theta. Or is that not so important?
You're right, but it is just notation. I'm actually facing this problem because every book or video I read/watch has a different notation, slowing the learning process.
Good work, sir.
You are a hero.🤣
Very clear!
Awesome
Cool!
H do we solve this equation: v[k[=e[k]+1.4e[k-1]+0.38e[k-2]???
damn... this goes hard
Hi, I was wondering how invertability useful - what can I do with that information
thank you
you 're great thx
That's cool.
Bro you save my ass again!
When I look at that causal diagram, all I see is a RNN
Ya le errás aquí. Tenés que preparar mejor estos temas. Thanks 🌹🌹
Sir why we put log operator on time series variables like log gdp log cpi log oil price... What is the benefit of putting log.. Plzz answer sir
Exponential time-series cannot be studied properly. Logging them removes the exponentiality
Such a great video. Thank you!
Brilliant!
Thanks!
Thank You