Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings
利用谱包络与最优赋值进行类别时间序列分类
This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the spectral envelope and its corresponding set of optimal scalings. These quantities characterize oscillatory patterns in a categorical time series as the largest possible power at each frequency, or spectral envelope, obtained by…