The detrended series is checked for stationarity. Case 4: KPSS indicates non-stationarity and ADF indicates stationarity - The series is difference stationary. Differencing is to be used to make series stationary. The differenced series is checked for stationarity. Here, due to the difference in the results from ADF test and KPSS test, it can The library that I'm using is tseries and the function is kpss.test. I have done a simple test using cars (a default matrix on R). The code is: Contradictory results of ADF and KPSS unit root tests. 2. Check for stationarity of a time series and check Granger causality test. An interpretation of each test definition would be so helpful for me. Here's the plot of my time series: The tests in R (I'm using tseries library) gave me these results: for ADF test: data: timeserie Dickey-Fuller = -5.3593, Lag order = 8, p-value = 0.01 alternative hypothesis: stationary for KPSS test:
如果我们不能拒绝零假设，我们可以说该序列是非平稳的。. 这意味着序列可以是线性的或者差分平稳的（我们将在下一节中了解更多关于差分平稳的信息）。. dfoutput = pd.Series (dftest [0:4], index= ['Test Statistic','p-value','#Lags Used','Number of Observations Used']) ADF检验结果
bds.test BDS Test Description Computes and prints the BDS test statistic for the null that x is a series of i.i.d. random variables. Usage bds.test(x, m = 3, eps = seq(0.5 * sd(x), 2 * sd(x), length.out = 4), trace = FALSE) Arguments x a numeric vector or time series. m an integer indicating that the BDS test statistic is computed for embedding di-
KPSS: The timeseries fails to reject the null hypothesis of stationarity (wtf? how can this timeseries be stationary if it clearly has a tendency upwards?) ADF: Can't reject tau (at 1%) and therefore there is a unit root, reject phi2 and therefore there must be drift, trend or both. Gz3He. 495 338 301 499 377 487 97 423 353

kpss test vs adf test