I have a detrended series where the ACF and PACF has lags all within the 95% confidence bounds. This would suggest the series is a White Noise. However, fitting it to an ARMA model (in R) gives the following output:
Coefficient(s): Estimate Std. Error t value Pr(>|t|) ar1 ,0.9871, 0.0036, 271.907, < 2e-16 ma1 ,-0.9949, 0.0022, -435.129, < 2e-16 intercept ,0.0024, 0.0006, 3.546, 0.000392 Fit: sigma^2 estimated as 0.03175, Conditional Sum-of-Squares = 158.74, AIC = -3054.03
Thus I would be inclined to think it is ARMA(1,1), not white noise. How do I combine these two pieces of information?