Discrimination and Classification of Nonstationary Time Series Using the SLEX Model
利用 SLEX 模型判别与分类非平稳时间序列
Statistical discrimination for nonstationary random processes is important in many applications. Our goal was to develop a discriminant scheme that can extract local features of the time series, is consistent, and is computationally efficient. Here, we propose a discriminant scheme based on the SLEX (smooth localized complex exponential) library. The SLEX library forms a collection of Fourier-type bases that are simultaneously orthogonal and localized in both time and…