and I run the following tests:
> adf.test(x) Augmented Dickey-Fuller Test data: x Dickey-Fuller = -70, Lag order = 70, p-value = 0.01 alternative hypothesis: stationary Warning message: In adf.test(x) : p-value smaller than printed p-value > kpss.test(x) KPSS Test for Level Stationarity data: x KPSS Level = 30, Truncation lag parameter = 100, p-value = 0.01 Warning message: In kpss.test(x) : p-value smaller than printed p-value > Box.test(x, type = "Ljung-Box") Box-Ljung test data: x X-squared = 20000, df = 1, p-value <2e-16
How can this time series be stationary? I suppose problems arise because my time series is irregularly spaced. In this case, there exist methods to assess stationarity with irregularly spaced time series?