It should be frequency=1, to be more clear, you always need ask you to yourself, How many months, (another period of time) is there in a year? And you get the answer
Well, I'd use sarima only If the data is expected to be seasonal. But in any case arima is a particular case of sarima if the coefficients of the seasonal part are zero
@@MarioCastroPonce Thank you. Actually I wanted to say is it possible to use both together technically? Because only sarima is insufficient in my work.
Great video, outlining the steps at the beginning make what I'm learning in my time series class very clear
Thank your for your comment!
Thank you so much. You make it clearer.
Very clear. Thanks.
Is there way to calculate lambda for data having zeroes in it? Like rainfall data .
What it means the lag parameter in diff() and what value should I use for?
If we are running the analysis where we have weekly data, then how do we decide the lag in the diff() formula?
If you see 7 day correlations in the ACF, then add the parameter lag=7 to diff ()
@@MarioCastroPonce can you a share a link to study the concept?
what if lambda = -0.999 ?
If there is unit root, is this method still valid?
No. In that case, probably you would have to increase the order of the model.
For annual data,what will be the frequency?? Here monthly data frequency is 12
It should be frequency=1, to be more clear, you always need ask you to yourself, How many months, (another period of time) is there in a year? And you get the answer
Can we ARIMA and SARIMA be used at the same time?
Well, I'd use sarima only If the data is expected to be seasonal. But in any case arima is a particular case of sarima if the coefficients of the seasonal part are zero
@@MarioCastroPonce Thank you. Actually I wanted to say is it possible to use both together technically? Because only sarima is insufficient in my work.