Most established techniques that search for structural breaks in time series have a difficult time identifying small changes in the process, especially when looking for narrowband frequency changes. The problem is that many of the techniques assume very smooth local spectra and tend to produce overly smooth estimates. The problem of oversmoothing tends to produce spectral estimates that miss slight frequency changes because frequencies that are close together will be lumped into one frequency. The goal of this work is to develop techniques that concentrate on detecting slight frequency changes by requiring a high degree of resolution in the frequency domain.

中文翻译：

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AutoSpec：检测时间序列中的窄带频率变化

大多数在时间序列中搜索结构中断的成熟技术都很难识别过程中的微小变化，尤其是在寻找窄带频率变化时。问题是许多技术假设局部光谱非常平滑并且倾向于产生过于平滑的估计。过度平滑的问题往往会产生错过轻微频率变化的频谱估计，因为靠近在一起的频率将集中到一个频率中。这项工作的目标是开发技术，通过要求频域中的高分辨率来专注于检测轻微的频率变化。