A stationary time series is a sequence of data points collected over time, where the statistical properties of the data do not change over time. This means that the mean, variance, and autocorrelation of the data remain constant.
There are several statistical tests that can be used to determine if a time series is stationary, such as the Augmented Dickey-Fuller (ADF) test and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. These tests look for significant changes in mean and variance over time.
Having a stationary time series is important because it allows for more accurate and reliable predictions and analysis. Non-stationary time series can lead to spurious correlations and misleading results.
There are several techniques that can be used to make a non-stationary time series stationary, such as differencing, detrending, and transforming the data. These methods aim to remove any trends or seasonality in the data.
Yes, a time series can exhibit both stationary and non-stationary behavior. In this case, different methods may need to be used for different parts of the time series in order to make accurate predictions and analysis.