Multi-Variate Time Series Forecasting (VAR Model)| Complete Python Tutorial

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In this tutorial i guide you through a multi-variate time series forecasting project. I implement the Vector Auto Regression(VAR) model in python. I cover:
1. Preparing data for time series analysis
2. Converting non-stationary data to stationary data
3. Running the granger causality test for vector auto regression
4. Visualizing your time series data
5. Using VAR class to find order of your time series
6. Forecasting into the future using the VARMAX class in python

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Nachiketa Hebbar

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

junoli

Ah brother again. I don't know why but I think though there are many turtorials on YouTube 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

maaleem

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.

pawankulkarni

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!

brandyhorne

this playlist is gonna be a great help for my interview today amazing content brother

harshraj.a

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

bhavinmoriya

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

rolind

Fantastic Video. Have a fantastic day Nachiketa

zscvkkh

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

tagoreji

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.

shrutiiyyer

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

rawoofmohammed

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 ?

subrahmanyamkesani

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

Bhavyaa

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.

vidhyaa

Helpful, thanks for explaining each line

ajayrao

This is very helpful. thanks for sharing the video

SannidhiPHebbar

can you create a video for multivariate time series forecasting using GRU and LSTM

oladelemayowa

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?

BryanLee-fl

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

hanooltari

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

khaqim

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