CODEWITHSUNDEEP
time-seriesarima
Peder Ward
Peder Ward

Reputation: 89

Hard time finding SARIMA parameters from ACF and PACF

Im a beginner in time series analyses. I need help finding the SARIIMA(p,d,q,P,D,Q,S) parameters. This is my dataset. Sampletime 1 hour. Season 24 hour. S=24 enter image description here

Using the adfuller test I get p = 6.202463523469663e-16. Therefor stationary. d=0 and D=0

Plotting ACF and PACF:

enter image description here

enter image description here

Using this post: https://arauto.readthedocs.io/en/latest/how_to_choose_terms.html

I learn to "start counting how many “lollipop” are above or below the confidence interval before the next one enter the blue area."

So looking at PACF I can see maybe 5 before one is below the confidence interval. Therefor non seasonal p=5 (AR).

But I having a hard time finding the q - MA parameter from the ACF. "To estimate the amount of MA terms, this time you will look at ACF plot. The same logic is applied here: how much lollipops are above or below the confidence interval before the next lollipop enters the blue area?"

But in the ACF plot not a single lollipop is inside the blue area.

Any tips?

Upvotes: 0

Views: 1176

Answers (1)

Arne Decker
Arne Decker

Reputation: 923

There are many different rules of thumb and everyone has own views. I would say, in your case you probably do not need the MA component at all. The rule with the lollipop refers to ACF/PACF plots that have a sharp cut-off after a certain lag, for example in your PACF after the second or third lag. Your ACF is trailing off which can be an indicator for not using the MA component. You do not have to necessarily use it and sometimes the data is not suited for an MA model. A good tip is to always check what pmdarima’s auto_arima() function returns for your data:

https://alkaline-ml.com/pmdarima/tips_and_tricks.html

https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.auto_arima.html

Looking at you autocorrelation plot you can clearly see the seasonality. Just because the ADF test tells you it is stationary does not mean it necessarily is. You should at least check if you model works better with seasonal differencing (D).

Upvotes: 1

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