Your acf and pacf plot is ambiguous. The paradigm you are trying to follow to identify the SARIMA model sometimes works when 1) there are no one-time pulses in the data 2) there are no seasonal pulses in the data 3) there are no level/step shifts in the data 4) there are no local trends in the data 5) the underlying (waiting to be discovered ) model generates errors that has constant variance 6) the underlying model has constant parameters over time.

A more robust paradigm/procedure to follow is suggested here

https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf

If you post your data (which is always less ambiguous) , I will try and help further.

Your acf and pacf plot is ambiguous. The paradigm you are trying to follow to identify the SARIMA model sometimes works when 1) there are no one-time pulses in the data 2) there are no seasonal pulses in the data 3) there are no level/step shifts in the data 4) there are no local trends in the data 5) the underlying (waiting to be discovered ) model generates errors that has constant variance 6) the underlying model has constant parameters over time.

A more robust paradigm/procedure to follow is suggested here

https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf

If you post your data , I will try and help further.

Your acf plot is ambiguous. The paradigm you are trying to follow to identify the SARIMA model sometimes works when 1) there are no one-time pulses in the data 2) there are no seasonal pulses in the data 3) there are no level/step shifts in the data 4) there are no local trends in the data 5) the underlying (waiting to be discovered ) model generates errors that have constant variance 6) the underlying model has constant parameters over time.

If you post your data (which is always less ambiguous) , I will try and help further.

Your acf and pacf plot is ambiguous. The paradigm you are trying to follow to identify the SARIMA model sometimes works when 1) there are no one-time pulses in the data 2) there are no seasonal pulses in the data 3) there are no level/step shifts in the data 4) there are no local trends in the data 5) the underlying (waiting to be discovered ) model generates errors that has constant variance 6) the underlying model has constant parameters over time.

A more robust paradigm/procedure to follow is suggested here

https://autobox.com/pdfs/ARIMA%20FLOW%20CHART.pdf

If you post your data (which is always less ambiguous) , I will try and help further.