Time Series Talk : ARIMA Model

Intro to the ARIMA model in time series analysis.
My Patreon : www.patreon.com/user?u=49277905

[@Stefan-hl8fe]

Anchors...used to keep things stationary. I caught that pun.

[@ritvikmath]

Hahahaha, I didn't even intend that :) My viewers are clearly more clever than me

[@TheJuwailes]

@Castiel Lewis wow you managed to come off as a creep and an idiot in less than 25 words

[@troykhalil4270]

i guess Im randomly asking but does someone know a way to log back into an Instagram account? I was stupid forgot my password. I appreciate any tips you can offer me.

[@huxleyrodney3733]

@Troy Khalil instablaster =)

[@Eizengoldt]

@@huxleyrodney3733this is a clever scam

[@hameddadgour]

At 45 years of age, I finally understood what the ARIMA model does. Thank you!

[@TheLionSaidMeow]

I never thought I would be able to learn ARIMA so easily off of one side of a single sheet of paper. This was the most lucid explanation I've stumbled across. Subscribed!

[@milo1226]

This is exactly was I was looking for and was explained succinctly. Thanks for posting!

[@bestbest-qe3pw]

Thanks a bunch. You've done what my professor failed to do for a straight month in 9 minutes. Cheers to you

[@castro_hassler]

Nice vid, I've seen every time series vid, I got so much intuition , thanks

[@benoitl.8101]

Really simple and clear explanation of what I've been struggling to comprehend in the past few weeks. Many thanks from France

[@ritvikmath]

Glad it helped!

[@rockyjagtiani]

Great work. Your videos are great contribution to Students and Teachers , during this Lockdown period. Thanks.

[@willbutplural]

Loved the analogy with the anchor and clear breakdown of the equation! Subbed!

[@somanadhasatyadevbulusu9737]

You are a great teacher. Keep up the good work. :)

[@akashjain2694]

Probably the most clean video that explains ARIMA

[@abhishekv7171]

Well Explained Ritvik...Keep spreading knowledge!!

[@pedrocamunas5625]

Very clear and direct to the point, it helped me a lot, thanks

[@yuthpatirathi2719]

Amazing explanation Ritvik!

[@c4lb333]

I have an interview tomorrow that might involve time series knowledge, and your ARIMA, ARMA, ARCH, and GARCH series are really a life saver! They're explained very concise and clearly and saves me a lot of time looking through slides. Wish me luck LOL

[@laminann8061]

How was your interview? I hope it went well 😊

[@gustavosantanavelazquez7205]

You make it so easy to understand! Thank you!

[@MrJatind3r29]

You explained it so easily! Great Job!

[@lanjiang5564]

Thank you so much for such a clear explanation!

[@m.raedallulu4166]

Thank you so much, sir. I wish I found your channel long time ago.

[@cobbdouglas690]

Fantastic and intuitive explanation. Thanks!

[@AK-tj4ot]

You explained this so simply. Thank you so much.

[@ritvikmath]

Glad it was helpful!

[@rezajavadzadeh5597]

You're awesome, thank you so much for making these

[@hihi7896]

watch this man before every lecture to make sure I understand what's going on

[@bugravardal6432]

Excellent clear explanation, thank you very much. I think you have clarified what was a question mark in my head the last few days, that is whether the additional inverse transform would still be needed when the differencing was performed by arima itself. Could be obvious to some but wasn’t to me…cheers

[@high_fly_bird]

It's really great! You use only one paper sheet, and I basecally understood everything!

[@JJ-ox2mp]

You're an awesome teacher!

[@nitsuanew]

This is an awesome video for ARIMA model.

[@andreluisal]

Excellent!!! Congratulations!!!

[@HimanshuGupta-gl4ei]

Thanks, your videos are a great help.

[@dipeshkhati4895]

Saved the day for me! Thank you

[@user-cc8kb]

Very well explained! Thank you!

[@jorangeeraerts3047]

Excellent video, thanks!

[@vijayantmehla7776]

Very well explained.. Thank you !

[@mikeranelmagboo]

Thank you so much for this!

[@swastikkhadka6954]

Such a nice way to teach Thank you

[@lavidrori7518]

You are the best I ever saw!

[@tejaljadhav1275]

You explained it so easily!

[@Blue17918]

You are much better for lecturing TS than my professor.

[@benoitconley1126]

Thanks, super clear ! Merci from France !

[@ritvikmath]

You're welcome!

[@abrahamraja2088]

This helped me a lot, thanks

[@ritvikmath]

Glad it helped!

[@kachappillyjean]

This is what happens when people with the kanck of teaching gets their act together ! I have been banging my head after attending my Masters class that explained ARIMA. I really do not understand why these profs have to write a whole lot of math equations and read through it when all they have to do is to explain the concept just the way you did. This is the way to teach. Thanks for making my life a lot easier !

[@TheEngVibe]

Takeaway for myself: ARIMA is the model applied for the time series data, where there is time dependence. It has a more step if transforming from crrelation of x and time to the correlation of x and x(t-1) (it's precedence). And from the formular of linear regressiin, the diff of x and x(t-1) is const (slope). So it doesn't depend on time. The 3 critiera for a series that can be applied ARMA (stationary): constant mean, constant variance, no seasonality.

[@alteshaus3149]

Super video man!

[@manglem10]

Very different from others !! All the basics covered

[@sohailhosseini2266]

Thanks for the video!

[@wissales-safi4938]

Thank u so much .. I rly love u man!

[@sanjukumari6453]

Thanks for explanation of mathmetical equations of ARIMA model

[@ritvikmath]

Most welcome!

[@haolunshan5092]

super clear, thank you!

[@aryashahdi2790]

This guy is so damn good!!

[@ritvikmath]

this guy thanks you :)

[@sfundomabaso3200]

Wonderful videos you make. I'm just curious whether do u do these models on statistical programs such as R or Stata

[@neilhouse4591]

Great help. Thanks!

[@kostyamamuli1999]

Great tutorial man!

[@Ju-dk1eg]

Great teaching

[@longwenzhao9204]

amazing...so clear...

[@user-tt3lf4bx2c]

beautiful model

[@pianoista6464]

Thanks for the clear explanation. One questions though, in estimating ak where you need to find summation of Zk-i where i=1 to k-l, but how do we estimate Zl+1to Zk-1, as how do you know errorl+1 to errork-1?

[@terryliu3635]

Again, great explanation! Do you have any videos on multivariate ts analysis or prediction? Thanks

[@mengnixu7247]

thanks ! U explained clearly

[@ritvikmath]

thanks!

[@alecvan7143]

Great video! :)

[@ritvikmath]

Thank you!

[@TheEngVibe]

Many thanks 🎉❤

[@tracyyang1832]

Thanks for the great video. Very clear. One quick question, do we have to make sure the data to have no seasonality and constant variance to apply ARIMA model? Differencing, the I part, is to de-trend the data.

[@hbeing3]

Thanks! The second time I watched this video just to revise. A question regarding the final a_k value. 07:38 Is a_k= the sum of all delta + the inital known value instead of the last known value you show here? i.e. a_l should be a_(k-l), or a_0?

[@aanilpala]

I got confused at the same point as well. I think it should be a_0.

[@haow9020]

No, it should not. (k, a_k) is to the right of the last data point, i.e., (l, a_l); assume l=k+1 and you'll see.

[@phuonghanguyen7406]

thanks, It helps me very much

[@ritvikmath]

Glad to hear that!

[@shaswathpatil3439]

Thank you!

[@xwcao1991]

Man, you deserve a Prof. title

[@amira_369]

Best video!

[@kunalkiran3318]

When we had data till t=l, and we were trying to find the value for t=k, we need to a calculate a few Z (the summation of different Z). But for calculation of Z, we need the previous error. Since we do not have values after t=l, how do we calculate say Z at t=k of k-1?

[@sandeepmishra3275]

Amazing

[@davigiordano3288]

Thank you

[@officialmintt]

Thank you so much! May I ask for an example of an application/occasion where we might do the second difference?

[@krzysztofrozanski466]

Hi, sometimes when predicting house price indices, you might need to go with second difference to make them stationary (at least this happened to me once). I would not treat this as a rule for all house price indices in the world, however, as it for sure was "series specific". Hope this helped :)

[@Flyer111100]

hi awesome videos, just wanted to know if it is also possible to just multiply my zt value times my a value at t to obtain my future value?

[@sangaviloganathan5194]

I am a beginner. Correct me if I am wrong. For example if the pacf plot shows lag 2,4 and 6 as significant, will the AR model be of the order 6? if so, how does the insignificance of lag 5 get factored into the model

[@ritvikmath]

Thanks for the question! Indeed PACF showing 2,4,6 means you should include those lags in the AR model. By not including lag 5, we are saying that it is not important in "directly" predicting the current value

[@animeshtimsina3660]

@@ritvikmath If we use the order 6 then doesn't the model automatically include lags 1,2,3,4,5 and 6 in it? If this is true then how can we tell the model that lag-5 is insignificant but lags: 1 to 4 and 6 are?...PS. I am a beginner!

[@fpodunedin3676]

Note for self: an ARIMA model is the same as an ARMA model except that it will 'de-trend' data. This is through taking the difference of some a_t and a_(t-1) and then letting that be equal to your ARMA model.

[@qanhdang4035]

This explanation will be better if the notation used is consistent with the explanation on ARMA model. Also, for ARMA applied on z, likely it lacks the bias phi0 (which is beta0 in your ARMA explanation). Anyway, it's a good explanation of ARIMA.

[@kaiyanzhu3075]

I have a question, so in this video, the ARIMA is Stationary or non-stationary? or if it was transferred to the differences between a(t)-a(t-1)it will be stationary? Thank you

[@soufianebouabid2946]

okey ur awesome !

[@AlankritIndia]

shouldnt we add a constant term like phi(0) in Z(t) eqn..like we had in previous model for ARMA?

[@ahmadabdallah2896]

i thought the same thing

[@Bbdu75yg]

Nice !

[@user-yu1nd6qx4l]

In the bottom of your sheet, with sigma z(k-i), wouldn't the last component be z(l) which is a(l+1)-a(l) ? But I thought a(l+1) is a future value.. Did I miss sth ? Thank you so much for the videos, I'm going through all of them!!!

[@surajsinghdeshwal]

thankyou

[@samk3566]

What is the diff between differencing and removing the trend??? Does stationary simply lack of trend and seasonality??

[@ansylpinto2301]

Not entirely true but presence of trend will violate constant mean and seasonality constant variance. ARIMA models work well with stationary data so it is important the values used to model them do not have trend and seasonality.

[@sannederoever1320]

Writing out the equation for a_k, the logical conclusion seems to be that the equation ends with a_0 instead of a_l. Isn't a_l = a_{k-1}?

[@mmczhang]

that is what I thought as well

[@muhammadghazy9941]

@@mmczhang yep me too

[@joaojulio435]

I think it is, and the upper limit of the summation is k and not k-l (In my opinion). It makes more sense now, thank you for spotting this!

[@pallavibothra9671]

Please make video on RNN, LSTM..Eagerly waiting for that :)

[@dominikc2559]

Hey there! I've got a question to your z_t graph, i get the part, that the average of z_t should be positive, since we got a positive linear function. But if we compare the next value with the previous value, we should also get negative values within that graph? If we only get positive values, the initial graph should be monotone rising, but in your example its a noisy rising graph or am i getting something wrong? Best Regards

[@prevail8380]

At 5:49, is the order of I equal to 1? If so, how would the equation change if the order of I was 2 while the AR and MA orders remained 1?

[@15Mrtin15]

GOLD

[@michaelelkin9542]

Why is the MA part done on a() and not z() shouldn't both parts be on the stationary z() data? Thank you.

[@areebwadood6273]

Could ARIMA be used if the anchor chart had an exponential trend instead of linear ?

[@mithunim]

My guess is you can use ARIMA but instead of differencing the series once to make it stationary, you might have to difference it at least twice.

[@aimalrehman3657]

what is epsilon_t-1 in the MA bit of the ARIMA equation?

[@guilhermecoelho2354]

The "I" part is to be equal to 1 when we have a unit root on the time-series. Not when there is a trend !!

[@randall.chamberlain]

But if I take the original time series and apply a diff1 to make it stationary, couldn' I just apply an simpler ARMA model instead?

[@GriffinHughes-ss8tn]

goated

[@evrenbingol7785]

What if you want to predict so far into the future that K-i goes out of bound. say L is 100 and K is 1000. (Z sub K - i) would give you out of bound error since.(you are trying to go back to negative Ts, Since you do not have 900 Ts, So the assumption is you can only predict into the future as much as the length of your data? Is that correct.

[@ritvikmath]

Yes that is correct. Intuitively, you likely don't even want to predict out that far since your predictions probably won't be great.

[@ruifernando8066]

how to determine the value of p,q?

[@LukasHesse-po1ri]

why is a_k further down the x-axis then a_l? shouldnt it be the other way around?

[@nickcorona3966]

How do you calculate the errors?

[@jonasgreen1733]

good

[@4lex355]

it is not aL in the end but a1.

[@user-or7ji5hv8y]

How about cointegration? Is that useful?

[@shrikantlandage7305]

Thanks that was too straight forward.Good Work