A test for second‐order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series

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Evaluating time series forecasting models: an empirical study on performance estimation methods (2019, methodology): “• a boolean variable, indicating whether or not the respective time series is stationary according to the wavelet spectrum test [31].”
Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation (2015, methodology): “Adopting the Bonferroni correction as in Nason (2013), we rejected the null hypothesis of stationarity or normality when the corresponding p-value was smaller than α∗ = 0.”