Hello. I'd like to thank you for these videos and all the effort you put into creating them. I really appreciate your work.

For some reason acf() has changed. For an easy correction use: acf_vals = acf(first_diff, nlags=20) So you get the 20 needed data points.

Hi, It's a nice video. Can you make a video in which we need to decide p,d,q and P, D, Q on the same dataset in the same problem? Thank you.

Absolutely amazing. Great job of explaining it. I came here for the code but I am going to watch the whole video series on time series forecasting.

It would really help if you could explain how you decided AR(0) and MA(0) of the ARIMA(0, 1, 0), and the Seasonal AR(1) and Seasonal MA(1) of the seasonal component of (1, 0, 1, 12)

Hi ritvik. Thanks for the awesome videos. Can you create a video on how to decide SARIMA parameters- pdq and PDQ?

Why did you take my_seasonal_order = (1, 0, 1, 12) at 4:34? How did you find AR = 1 and MA = 1?

hey man. your videos are great and inspirational. If will be great if make a video about ARIMAX models. thanks

Thank you very much. This is very helpful.

I hope I could have a professor like you in my collage.

Awesome thanks a lot. I would really like to see a video like this about ARCH and GARCH. :-)

Thanks a lot. Can you explain how did you choose P, D and Q for the seasonal part?

please explain in another video the best way to choose P,Q,D & p,q,d

Thank you

Hey Ritvik, great content and great explanation. Can you please make a video on Vector AR?? and can you add the dataset or link please??

On your rolling forecast loop - doesn't SARIMAX in statsmodels predict one step forward by default? So for example a six month forward prediction out of sample would automatically predict one period forward, then use that prediction and the preceding data to predict a second step forward, etc?

Great explanation! Have a query - the data is sampled daily, the column to be forecasted is cumulative and has monthly seasonality. If I take the value of M=30, it works well for months with 30 days but get confused if the month has 31 or 28 days. How should I select the value of M?

Hi, could you tell us about handling null values in the dataset? Thanks. Great video btw.

Thank you for the clear explaination on time series forecasting. I tried to use the auto_arima class to identify the orders. My data contains multi seasonality and I was not sure which value for the m should use. Do you have any idea please?

Can you please explain what would the values be if the lags are not 12? would the p and q be different? From what i infer now is that since acf and pacf show 12 and m = 12, therefore P and Q is 1??? what if acf or pacf show lesser/more than 12 and what if acf is diff from pacf?

Unfortunately, it isn't "Coding the SARIMA Model" per title but rather "using existing implementation" without much extra.

Great video! Could you show us in your code an out-of-sample forecast?

Hello I have a forecasting and Anomaly detection module which uses ARIMA for modeling, Customer's data are streams of time series data stored in a database and the module first fetches history data to select the best model from a predefined sets of (p, d, q) and train it. my question is what is the min required history for ARIMA to work fine, and is there an equation to calculate it give the (p, d, q) values. mainly the data have daily and weekly seasonality.

Thank you!

Why did you choose m = 12. Is it because you have monthly data and each year pattern repeats?

I have a daily turnover data (for 6 years) and like yours it has a seasonal process of 1 year so 365 observations for my case. Do you think that I should take 365 as the number of lags?

Thanks. Could understand the concepts. But how to arrive at the right set of P,D,Q is not explained sufficiently...When I ran auto_arima, I got different set of PDQ which gave better results..

How many full cycles / years is needed to make SARIMA(x) viable? Is 1 and 1/2 years sufficient? Also its daily observations. So in total around (unique) 600 observations.

but if we have multiple seasonal pattern in our data then what will be the sasonal order in the sarima model?

what about for additive models instead of multiplicative ones?

great video

If I have daily data and every year pattern repeats, should I go for m = 365?

thank Boy!

Hello, is it not the differenced data that we have to use to fit the model?

Can we forecast beyond 2000-01 and how ?

Hello, im trying to predict employee churn time series, looking at my data i got a seasonal pattern every 7 days so a week (data is given dayly) but adjusting the SARIMA parameters i only got an AIC of 1446 is this too high?

Hy can you made a vedio mixtureof ARIMA+ANN and SARIMA+ANN

Hi, I don't know if you know how to do an arima model with only AR lag 5, I don't want the lag 1,2,3 and so on up to 5, how i can do that??

Thank you bro

think you

Hey Ritvik, can you share Catfish data link? Thanks

Used auto arima

Very stupid explanation