Ah brother again. I don't know why but I think though there are many turtorials on KZread I landed on your channel the reason I think is that I admire you coz you look like my age, and when a person gets a someone form whom he can learn whose age is same of his then it's a great pleasure and admiration

Excellent Brother!! Good Job you explained it very well :👏

Thanks Buddy, great content!

Life saver. Your explanation and demonstration is way better than ChatGPT.

great content. very helpful

Excellent content !!

Helpful, thanks for explaining each line

@NachiketaHebbar

3 жыл бұрын

you're welcome :)

Fantastic Video. Have a fantastic day Nachiketa

This is very helpful. thanks for sharing the video

Can you please include all variables and forecast the results with different forecasting algorithms. This one I found interesting. For multivariate forecasting can we use Arima ? I am newbie to forecasting. So any related help/ comment will be appreciated.

Great job man

Brilliant bro!

Helpful video 🎉

Excelentes videos nuevo seguidor :)

This is great - and I like how you go through the code. I am new to Python - transitioning from R - and understanding "why" to code something a particular way can be difficult when someone doesn't explain it. So thank you!

@maaleem90

Жыл бұрын

Madam, if you don't mind answering my query. Iam aged 24 and learning data science . I learnt R but just some basic stuff. Iam more shifted towards python coz I thought in future i can be able to switch my career from data science to some core programming and with python I learnt ML and deep learning stuff . What do you advise me on this. Is it a good thought or I should also be learning more of R.

Thanks, great stuff!

@NachiketaHebbar

2 жыл бұрын

Thanks!

Really helpful 👍

Good....Informative

Great work

Thanks for the fantastic video. If I were to use all columns how would I apply granger causality?

brilliant!

It was Simply Superb Bro 🙌. Also Nice Name 👍. Video with Detailed explanation of parameters would be Nice. Thank you

Love the parrots in the background! You could teach them to say AI, AI, AI.

Great video! I was wondering how you would test for white-nose residuals in the VAR vector error terms given that you got 4 lags to be optimal from AIC?

Thanks for sharing

good work

Pls some more on tutorial on multi variate forecasting ...time series. .like Dart , pycaret....thanks in advance...

If the variables are of different scales , is it imperative to scale them all like you do for typical ML models ? Good video btw. Thanks.

Thanks dude

Hi, Nice video!!. I have a query. At 10:40, Why "train_df" is simply used in VARMAX() function instead of "train_df.df()[1:] " ? What is the role of "enfore_stationarity=True" ? does "enforce_stationarity=True" makes the model to calculate and select such a "p" value where sufficient stationarity is available ?

Great explanation thanks, what about the p-values of each of the variables in the varmax model? I don't think all of those variables are significant from what I see in the summary of model fitting step.

감사합니다

hey lovely video, i wanted to ask that in ad fuller test why u used print(realgdp/realcons) when i am using the same on other data set it is not working, please help.

11:16 I have a question. so are the standard errors the values for all the residual error terms?

Is it helpful for forecasting sales with multiple dependent variable?

How can I write equation/formula based on that model result?

Hi Nachiketa, Thanks for the nice tutorial. While trying to replicate the exact steps, I faced and error while using the VARMAX function at 11:00. { ValueError: Non-stationary starting autoregressive parameters found with `enforce_stationarity` set to True. } What could possibly be wrong? I even tried removing that optional parameter, but, of course, the same output. Another doubt: in the first cell, while importing libraries, we can ignore importing the "tqdm". Right? Thanks again.

Hii, at 12:48 of the video, what is happening when we did "predictions= predict.predicted_mean" ? Which variables are included in the mean calculation ?? Can anyone explain please..

the p-value of independent variables in VAR output seem high, is that a problem? I read some similar examples of VAR but no one actually talk about this issue

TE AMO

what happens if you have one dependent time series with an integration order of 1 and the independent time series has an order of integration of 2?, how do you perform the granger test in that case?, do you have to differentiate the dependent time series one more time ?

Hi! Can you show how to make VAR model in Exel? Pls

What If we have Week instead of date column.... Can I use parse_date= 'Week'

While using Adfuller, my Colab notebook seems to crash due to complete RAM consumption(12GB) in the basic version of Colab. Please suggest me a solution. Data size is 0.3M records, 18 columns.

hii i am facing error while running the select_order thing...... TypeError: loop of ufunc does not support argument 0 of type float which has no callable sqrt method.............please help unable to solve this error

bro in prediction my year index column convert into numbers what need. to do please help

RMSE for ULC was at 54. which is high, how do we get that value down.

i don't get how he got the date to show up in the predictions column

hello at 8:28 why did you use the diff()[1:]? and not just the train_df ?

@marcosyamasaki

2 жыл бұрын

I think it's because VAR() model needs stationary data, so when you do train_df.diff()[1:], make it stationary as he has shown in adfuller test. At 11:00 he explains about VARMAX() model, which with the parameter enforce_stationarity = True, make it easier since you don't need to provide the stationary data (which afterwards you'll need to invert the transformation), cause the model already transform your input data for you. You can do the classic way using the train_df.diff() as input of regressor model, but it'll take more lines to do the same thing as he did.

5:43 so the dataset is stationary?

In 7:20, can you explain again, does ulc cause rgnp? I don't get it.

@junoli8294

Жыл бұрын

ulc doesn't cause rgnp at lag 1, but does in lag 2, 3, and 4

Y a ale tambien

Hey if your doing this with non stationary data and it came with good prediction, why doing stationary is necessary??

@xeiquespirwilliam2167

2 жыл бұрын

Because of consistency. I mean, if your data isn't stationary, you can't build a wide and general model.

@leelasaivoonna1728

2 жыл бұрын

@@xeiquespirwilliam2167 hey but what should we do for VARMAX how to know the Q value..

@xeiquespirwilliam2167

2 жыл бұрын

​@@leelasaivoonna1728 I'd ensure that the data is stationary before training the model. You can take the first difference and do ADF test to confirm that you're ok to go. And back to "why stationarity", these models like VAR and ARIMA have such a mean reversal behavior. The further your predictions go, the more it will look like a flat line. If your data isn't stationary, let's say it's incresing along time, the true data will take off to the roof while the predictions will stay in the middle, even if the short term perdictions, the generalization is affected.