Determine AR term from PACF plot I have the a time series data, the acf and pacf for which have been displayed below:


I get that MA term is 1. But I'm confused about AR term since it is geometrically decaying from 7th lag. Do I need to transform the data again to eliminate this?
PS: This data has already been differenced once.
 A: Only your data knows for sure as there may be a weekly deterministic effect (seasonal pulses) and other "features" waiting to be discovered .... post your data and I will try and help further .. The acf and pacf are descriptive but only inferential iff ...no pulses , no level/step shifts , no seasonal pulses . no local time trends , constant error variance and parameters over time
after receipt of your data ( 366 days ily values for 1 year )  .
In summary ...when you have daily data there could be


*

*day-of-the-week effects         not provable with your data

*week-of-the-year effects        not provable with your data

*month-of-the-year effects       provable

*day-of-the-month effects        not provable with your data

*week-if-the-month effects       not provable with your data

*holiday effects                 not provable with your data

*long weekend effects            not provable with your data

*level shift effects             not significant

*local time trends               not significant

*arima effects                  not significant

*differencing effects           not significant

*pulse effects                  significant

*paramater changes over time    not provable with your data

*power transform effects        significant


15  deterministic error var change not provable with your data
16  exogenous variables effect     not provable with your data
The software I used identified a number of pulses ...1 period anomalies . See the Actual and Cleansed graph here  . The cleansed series led directly to a seasonal model with 10 predictors using a log transformation .
The plot of the residuals is here  suggesting reduced variablity during the summer months. The residual acf is here 
Responding directly to your question the best arima model is (0,0,0)(0,0,0) with 10 Monthly Seasonal Indicators reflecting deterministic changes through the year.
The pulses that were identified are here :  and model summary here  
