La prueba Kwiatkowski-Phillips-Schmidt-Shin (KPSS) determina si una serie de tiempo es estacionaria alrededor de una tendencia media o lineal , o si no es estacionaria debido a una raíz unitaria . Una serie temporal estacionaria es aquella en la que las propiedades estadísticas, como la media y la varianza , son constantes a lo largo del tiempo. $\begingroup$ With this sample size, I would mainly be concerned about a potential lack of power. However, given that the test is significant, this does not appear to be an issue. Also, adf.test(y) cannot reject the null hypothesis of a unit root which is consistent with the kpss.test() result. My take would be that the series looks like a random walk with drift which is also what auto.arima(y 5.4.2 Test the anchovy data. Let's try the anchovy data. kpss.test(anchovyts, null = "Trend") KPSS Test for Trend Stationarity data: anchovyts KPSS Trend = 0.14779, Truncation lag parameter = 2, p-value = 0.04851. The null is rejected (p-value less than 0.05). Again stationarity is not supported. These are the computer labs for Fish 507 Because the distribution has zero mean you can just use the absolute value of both test value and critical value: |kpss_val| > |critical_value| = null rejected. You may also use the p value which is returned by the statsmodel implementation. Note that it's only in the range [0.01, 0.1]. It is highly recommended that KPSS and ADF Test are used for testing stationarity in the data. Hence, the following aspects might arise if using both the tests :-1. ADF and KPSS Test conclude that 平稳性检验常用方法有ADF检验和KPSS检验。 平稳性检验-ADF Test. ADF Test：Augmented Dickey-Fuller Test 首先假设时间序列是不稳定的，根据假设求得的置信度P值如果小于阈值（一般为1%），那么我们认为假设不成立，时间序列是稳定的；反之，假设成立。 The autocorrelation is decreasing almost steadily, this points to non-stationary data also. Doing a kpss.test should verify that our data is non-stationary, since its null-hypothesis is the opposite of the adf test. Do we expect a value smaller than 0.05. kpss=kpss.test(ts[1,]) kpss We receive a p-value of 0.01, further proving that the data ADF (Augmented Dickey-Fuller) and KPSS (Kwiatkowski-Phillips-Schmidt-Shin) are two common statistical tests for stationarity. The purpose of these two tests is technically the same, except for the technique used behind. Augmented Dickey-Fuller Test table. The Augmented Dickey-Fuller Test table provides the hypotheses, a test statistic, a p-value, and a recommendation about whether to consider differencing to make the series stationary. The test statistic provides one way to evaluate the null hypothesis. Test statistics that are less than or equal to the Details. ndiffs uses a unit root test to determine the number of differences required for time series x to be made stationary. If test="kpss", the KPSS test is used with the null hypothesis that x has a stationary root against a unit-root alternative. Then the test returns the least number of differences required to pass the test at the level alpha.If test="adf", the Augmented Dickey-Fuller Mr67.