论文标题
通过多元循环奇异频谱分析理解波动
Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis
论文作者
论文摘要
我们引入了多元循环奇异频谱分析（M-CISSA），以提供一个全面的框架来分析波动，提取一组时间序列的基本组件，从而散布其变异来源并评估其在每个频率下的相对相或周期性位置。我们的新方法是非参数，可以应用于相位的串联，高度非线性和频率和振幅调节。我们证明，在共同信息的情况下，并且不需要拟合因子模型，我们可以识别常见的变异来源。在气候学，生物识别技术，工程或经济学等几个领域，此技术可能非常有用。我们通过在幅度和频率下调制的潜在信号的合成示例，以及通过对能源价格的真实数据分析来显示M-CISSA的性能，以了解在不同时间范围内评估能源政策的关键的主要驱动因素和主要能源商品价格的共同转移。
We introduce Multivariate Circulant Singular Spectrum Analysis (M-CiSSA) to provide a comprehensive framework to analyze fluctuations, extracting the underlying components of a set of time series, disentangling their sources of variation and assessing their relative phase or cyclical position at each frequency. Our novel method is non-parametric and can be applied to series out of phase, highly nonlinear and modulated both in frequency and amplitude. We prove a uniqueness theorem that in the case of common information and without the need of fitting a factor model, allows us to identify common sources of variation. This technique can be quite useful in several fields such as climatology, biometrics, engineering or economics among others. We show the performance of M-CiSSA through a synthetic example of latent signals modulated both in amplitude and frequency and through the real data analysis of energy prices to understand the main drivers and co-movements of primary energy commodity prices at various frequencies that are key to assess energy policy at different time horizons.
