Time Series Talk : Moving Average Model

A gentle intro to the Moving Average model in Time Series Analysis

[@yassineaffif5911]

i wish my professor had explained it exactly like u just did

[@lexparsimoniae2107]

Thank you very much for making a vague concept so clear.

[@chiquita_dave]

This was extremely helpful!! Between my 3 econometrics textbooks (Griffiths, Greene, and Wooldridge), the information on MA models was sparse. This really cleared up the mindset behind this model!

[@rezvaneaghayan3129]

God Bless You! I needed a fast way to get some concepts on time series forecasting and you saved me. Easy, Fast, Complete.

[@rachelzhang9691]

Thank you so much for making this fun video! Makes so much more sense now (after struggling through my not-so-crazy professor's stats class)

[@m.raedallulu4166]

I really don't know how to thank you for that great demonstration! I've been trying to understand MA process for years!

[@lotushai6351]

Thank you so much for your very intelligent explanation to this model!!! i felt so confused about this model before.

[@richardr951]

Thank you Sir. You have a great way of explaining things, something I sadly rarely find from my coding/statistics teachers.

[@juanignaciox_]

Wow! Great explanation. The professor´s example was very intuitive. Thanks for the content!

[@yordanadaskalova]

Never seen a better explanation of MA models. Immediate subscription!

[@nicop175]

Same here! I knew I would suscribe after 1 minute in the video. Very clear and very useful video. Thank you very much.

[@dboht4200]

So simple yet easy to understand. Thank you!

[@patricktmg4372]

Finally ❤️ a video with an applicable and relevant example ❤️🙏

[@shadrinan90]

Great explanation! I've learned everything that I looked for. Thank you.

[@pastelshoal]

Fantastic, got too caught up in the math in my macroeconometrics course and had no idea what these things actually were. Super helpful conceptually

[@vinayak_kul]

Oh damm!! this is wonderful, Simplified and explained pretty nicely. Keep spreading you knowledge!!

[@ritvikmath]

Thank you! Will do!

[@jahnavisharma1111]

ALWAYS GRATEFUL, THANK YOU FOR THE WONDERFUL CONTENT

[@tiffanyzhang4805]

Thank you so much for explaining this so well! My professor and textbook explain this concept very mathematically which is hard to understand for beginners, they should really give a simple example and then dive into the details as you did.

[@ritvikmath]

Glad it helped!

[@beatrizfreitas7363]

Finally understood this, thank you so much. Highly recommend!

[@lima073]

Simple and clear explanation, thank you !

[@Manapoker1]

I was terrified for the mathematical symbols, but you made it so easy to understand! thank you!

[@jhonmaya7264]

a year trying to understand this, and I ve just needed 15 minutes thx!!

[@wanjadouglas3058]

This was the best video on MA. The crazy prof made our life easier 😂😂😂

[@plamenyankov8476]

You are spectacularly GOOD in the explanation of the ARIMA! Cheers

[@ritvikmath]

I appreciate that!

[@wycliffebosire4114]

Thank you so much, I have been reading this concept in an Econometric book...but this is easy to comprehend

[@reality2304]

OMG, this is brilliant , amazing ,wonderful ,thank you

[@alphabeta2723]

This men's explanation is way better than those profs at University.

[@jacobs8531]

Simple Explanation is a Talent - Thanks for this

[@BenevolentKhalluudi]

Awesome explanation! Thank you so much.

[@chenwatermelon5478]

I was stuck where is the “error" term coming from. Now I know... it is the error from the past. You explained! I wish you were my professor.

[@JJ-ox2mp]

Great explanation. Keep up the good work!

[@Sylar1911]

I love this video, so simple but effective

[@hakkin9787]

Thanks man. You're doing a suberb job.

[@tomasw8075]

Brilliant explanation, thank you!

[@tsetse4327]

Thank you very much! Such a clear explanation!

[@denisbaranoff]

This explanation gives better understanding why do we need avoid unit root in Time Series predictions

[@yuanyao972]

this is really helpful and so easy to understand!!!

[@K_OAT]

Nice example super easy to understand the concept!

[@sohailhosseini2266]

Great video! Thanks for sharing!

[@emreyorat803]

Manyt thanks for your clear explanation of the mathematical moving average formula

[@ritvikmath]

of course!

[@clapdrix72]

Extremely well explained

[@noeliamontero3839]

Thanks!!! Perfect explanation :)

[@yuthpatirathi2719]

Amazing explanation man

[@paulbearcamps]

Exceptionally useful videos for actuarial exams. Thanks for helping me pass🙂(hopefully)

[@sirabhop.s]

Greatly explain!!! Thanks

[@zairacarolinamartinezvarga1070]

LOVE IT. Thank you.

[@ritvikmath]

Of course!

[@tancindy2390]

you are just amazing

[@ZakharovInvest]

Great videos, thank you! I have a question. Period 1 value is our mean value but we don't know what is mean since we just started from point 0. How to calculate residual then? We know the true observation and we don't know the mean. Is it just a guess? But when we use any statistical package it does not ask us to input guess mean value.

[@urielnakach4973]

Explained with the Cup Cakes it makes perfect sense, thumbs up!

[@alexcombei8853]

[@fksons4161]

God Bless you.

[@maydin34]

Perfect!

[@erickmacias5153]

Thanks you so much.

[@user-eo7zd9dy2s]

thanks! Really helpful

[@jacqueline190]

THANK YOU SO MUCH

[@swiftblade168]

Excellent explanation

[@shei9413]

Thank you for the video, how should we choose the 0.5 coefficient in front of the error term from last period in the regression model?

[@jubaerhossain1865]

Hi, great explanation! One question, how do you guess the mu value (the average cupcake you bring) for the fist time?

[@krishnabarfiwala5766]

Amazing explaination

[@SS-xh4wu]

Thank you. Love your video tutorials! Just one question: shouldn't the curve at 5'58'' be f_t? And c(10,9,10.5,10,11) be f_(t-1)?

[@whoami6821]

thank you so much

[@manojsebastian2000]

Great Presentation...

[@ritvikmath]

Glad you liked it!

[@ChintuPanwar-fs8eu]

Well explained ❤

[@rishansarma7413]

Thank you❤❤❤

[@taylerneale7250]

Thanks this is a really clear explanation. My only question is when you are calculating your f_t column, why are you including the error from the current time period? Shouldn't you only be including the 0.5*e-t-1?

[@yvesprimeau6031]

So not natural.. it is why you are so good in teaching

[@niveditadas2372]

Wonderful example.

[@ritvikmath]

thanks!

[@nathanzorndorf8214]

Great video. Do you always start with the mean as your first guess for f hat? Also, how do you fit an MA(q) model?

[@rayevp3276]

THANK YOU!!

[@ritvikmath]

You're welcome!

[@nikhilchowdhary8919]

you are too good

[@oanabruntel2520]

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[@mikemedina7810]

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[@christinaray3698]

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[@christinaray3698]

If you can get me a link to speak with him, I'd greatly appreciate it.

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[@mercymuenimwangi9935]

Perfect.

[@uyenpham7928]

thank you so so much

[@ritvikmath]

You're welcome!

[@haiderwaseem7188]

Great video. I think the calculation of the 3rd row is wrong. It should've been 9+0.5 = 9.5

[@abhradeblaskar9666]

No.. Constant term is 10 not 9

[@THUYBui-dx2uc]

Thank you so much for making this video. I am so frustrate to understand this concept :(

[@jessicaleka1848]

sameee

[@khalilboughzou3092]

Hey amazing Content Bravo ! Can you add to that a video talking about random walk ? That would be great .

[@fmikael1]

how is it possible you can explain this stuff so easily!

[@MrPyas]

Had I watched your series earlier would have saved me $3000 :(

[@pratibhasharma8457]

Amazzzziiiiinnngggggg

[@aalaptube]

Observation: 5:32 Its always centered at 10 because the errors mean was 0 (per 1:02) and error was multiplied by Φ, which will have have a mean of 0. Feeling a little awkward commenting multiple times. Just trying to understand more by thinking aloud, and that someone may correct my understanding. :) Great videos!

[@anaradovic1519]

I saw the same thing, think it was just his mistake in calculation

[@ghazypheda]

THANK you

[@ritvikmath]

You're welcome!

[@matejfoukal9994]

Let's use an example that is sligtly more natural to us -- so here's this crazy professor. :D

[@nichoyeah]

Really good explaination! Maybe I'm stupid for asking this... If one was to write an MA filter, how do you determine M?

[@ashutoshpanigrahy7326]

God-like!

[@ritvikmath]

Thanks 🙏

[@vershaskitchen6059]

Awesome

[@AyushAgarwal-YearBTechElectron]

If a physics student is reading this, just wanna share my intution that this is exactly like a control system . whatever error our model is getting, it is moving to cover it , little bit like PI controller in Electrical engineering :) not sure if it clicks to anyone

[@zoozolplexOne]

cool !!!

[@thesofakillers]

How is the average moving though? It was fixed for each prediction! Wouldn't it have to be recalculated each time for it to be moving? Also we didn't seem to use anything related to the error being normally distributed... is there a reason for that? why was it mentioned in the first place?

[@ravikumarhaligode2949]

Exactly right, I am also having same query, Average not moving

[@ravikumarhaligode2949]

Did you get any other source where this explained clearly

[@RD-zq7ky]

What does it mean when the MA(1) estimated parameter = 1? For AR(1) that would mean there's a unit root. Any particular corollary for MA models?

[@vivekkumarsingh9009]

Where does the noise in the equation come from? In our data we only have time on the x axis and Y as the target variable. There is no error term. What I mean to ask is does the MA model first regress y on y lag terms like the AR model and then calculate error between the actual and predicted y terms? Then regress y against the calculated error terms(residuals)?

[@manuelcaba2]

The error is a white noise coming from random shocks whose distribution is iid~(0,1). Ftting the MA estimates is more complicated than it is in autoregressive models (AR models), because the lagged error terms are not observable. This means that iterative non-linear fitting procedures need to be used in place of linear least squares. Hope this helps :).

[@siddhant17khare]

Does MA model assume et (lagged residuals) are pure white noise ? Mean =0, constant variance , and no autocorrelation of residuals ?

[@edavar6265]

This is a great explanation but in many equation they also add the current error (epsilon_t). I just don't get how are we supposed to know our current error if we are trying to forecast a value. Do we simply neglect that current equation for forecasting?

[@TehWhimsicalWhale]

How do we know what the "error" is there is if there is no "true value" given a random realization of data.

[@pepesworld2995]

the idea is that you're trying to predict the next value. you get told what the next value is by the professor. if its random then there is no signal in there & the results are still meaningless

[@chunyinlee2542]

Thanks goddd

[@wolfgangi]

I still don't think this makes sense to me why is incorporating past error somehow gives us better prediction in the future in this case. Since this crazy professor will randomly choose an acceptable # of cupcakes, your past error shouldn't help in better predicting in the future.

[@vitorgfreire]

I think the student naively believes the crazy professor will stick to his prior t-1 position (the student is unaware of the professor's craziness)

[@jeongsungmin2023]

Everything in time series assumes that you can use past info to predict future info

[@marzi869]

Event though the professor selects a different number every time, at the end the average is stable. Assume you have a time series of images. Images, due to the unstable environment they're taken in or all other factors that manipulate images nature, are not always the same, although they are taken from the same scene. So, what is the goal here ?to find the mutual information in the images and ignore the noises. These noises are how crazy professor is , and the importance of error, which we can handle by its coefficient. By handling these factors, we can get close to recognising the mutual information. Remember, these are unsupervised models. There are no lable to rely on.

[@GauravSharma-ui4yd]

Sir please make videos on restricted Boltzmann machine

[@jayjayf9699]

How come some MA(1) formulas have x_t = mu + (phi1) error_t + (phi2) error_t-1..... If you predicting at time t then how would you know error at time t (error_t), why are some formulas like this?

[@alisadavtyan2133]

Hi. The mean of et is not 0. For time interval 5, you need to write -1.

[@ranitchatterjee5552]

How is mean determined? BTW, it was a great video! Thanks a lot!

[@tenalexandr1991]

I really like your videos. They work very well for me, someone without any background in time series. However, this one is somewhat confusing. You are demonstrating the concept of *moving average* with an example where the average stays the same. I get that the estimate moves around, but that is due to the error variance, right? The average itself is not moving anywhere. Both mu and mu_epsilon are assumed to be constant, so what's moving here?

[@supriyabhatia4953]

Thank you Ritvik. Is there any recommendation on books for Time Series. I am currently in school doing my Masters and I am feeling all over the place with this subject. Any suggestion on how to crack this one will be appreciated.

[@manuelcaba2]

Hi, I studied a Master in Quantitative Economics and I used this book: Econometric Modelling with time series by Gloria González Rivera. Feel free also to send me an email if you want some problem sets to practice. Best from Spain.