Wavelet methods are used to model time series data that change over time. The researchers developed a way to estimate both the trend and structure of nonstationary time series using wavelets. They also created a method to model time series with changing mean and autocovariance, allowing for more accurate trend detection. The new approach was tested through simulations and showed good performance in various scenarios.