MA Model Code Example : Time Series Talk

[@yuthpatirathi2719]

Great video as ususal . These videos are superhelpful as a grad student to me and I am truly grateful for your explanations

[@mehradghazanfaryan640]

Great Great . do not stop the lectures on time series , they are great thank you

[@BBB_025]

I think there is a huge amount of value in seeing how an artificial time series data set would be created. I think this is a strong re-enforcement of the concepts you have taught in the time series playlist. I could see it being an interesting/useful excerscise/video for you to provide an artificial time series data set, ask your viewers to fit a model to it, and then you would provide a solution video to correctly fitting the model or in this case, potentially deriving the correct function that generated the artificial time series data to begin with.

[@chandrasekarank8583]

Even i agree with this . Please man make videos on arima using synthetic data sets

[@maxhunt3050]

These vids are awesome! Thanks so much, you deserve more views!

[@esijal]

Excellent presentations 🙏

[@__goyal__]

Thanks a ton for some very good pointers!

[@dianaayt]

5:50 but if we have an actual time series and not a function, how do we know if those values are close to the correct ones?

[@siddhant17khare]

A) As per my understanding, the following are the 3 scenarios : a) Actual Time series data vs Its lagged values :- This is ACF b) Actual Time series data vs Its lagged residuals : This is MA model (Example : Yt = μ + εt + θ1εt-1) c) Residuals after fitting any model(say AR) vs its lagged residuals : This is the check for white noise that we do on residuals to ensure that there is ~0 autocorrelation amongst the residuals. Doubt 1 : When ACF & MA models represent 2 different things(as mentioned in a) & b) respectively), then why is ACF used to determine the order of MA models ? B) As per MA equation : Yt = μ + εt + θ1εt-1 where εt is said to be white noise . Therefore, this MA Model, models the relationship between actual time series data and the white noise terms. Doubt 2 : When εt is already white noise (for example obtained from the AR model), then why do we need to model it in the first place ? Isn't the residual being white noise from the AR model enough to ascertain that nothing else can be modelled as the residuals are white noise ?

[@siddhant17khare]

A very basic question: If in the example you illustrated- If the error was already white noise, what was the rational of building a model on top of the mean value(50) ? Since , as a diagnostic norm - we always look whether the residuals are white noise or not and here in this example if the residual or error is already white noise, why is there even a need for MA Model ?

[@Unremarkabler]

this test is based on assumption of 2 randomly related data, so the random data is actually connected with each other,which is not truly random

[@madhusharma-ee3hv]

why do we use acf for calculating order of MA process

[@jongcheulkim7284]

Thank you.

[@NARFkarriere3]

Is there at all any logical link between the actual observed data (time series) and the MA-model? The error term seems to be generated by random numbers with mean zero and a standard deviation of one, that is a standard normal distribution. So if this has nothing to do with the time series you actually want to analyze and forecast, how do you compute the coeficcients in the MA-model? To me AR models make perfect sense, but MA-models are very confusing. Thanks in advance for any feedback or advice :) O

[@zagwask78]

I have wondered the same thing. It's perhaps explained in another video but I don't know. I'm having trouble figuring out what order to watch these in. I can't figure out a way to sort by upload date (even if that's the order they should be watched in). Watching in the order they show up in in not optimal.

[@zagwask78]

Has anyone figured out what order to watch the Time Series Talk playlist in? The videos themselves are amazing as others have pointed out, but if I could only figure out the order it would be even more helpful.

[@njwu4117]

How are the coefficients of the error terms determined? Is there any rule? Are they given arbitrarily?

[@joaovictorf.r.s.1570]

Great video. I have a question, when i print model_fit.summary() i have: N Tit Tnf Tnint Skip Nact Projg F 3 7 9 1 0 0 2.220D-08 1.334D+00 F = 1.3335280824818536 What f means? i know if fcalculated>ftabulated (f fisher), the model is apropriate.Is this the case? But, we have in this example, i used (series.append(mu + 0.421*errors[t-1] + 0.234*errors[t-2]+errors[t])), with mu==20. ten lags and arima(0,0,2). with i have 121 observations, i have F(1,120)=3,92 Ttabulated>Tcalculated, my model is bad or not works?