通过多元循环奇异谱分析理解波动
Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis
AI总结：
本研究提出非参数的多元循环奇异谱分析（M-CiSSA）框架，可提取时间序列潜在成分、厘清变异来源，无需拟合因子模型即可识别公共变异源，通过合成与能源价格真实数据验证了其在多领域的应用价值。
摘要：
我们提出多元循环奇异谱分析（M-CiSSA），提供一个分析波动的综合框架，可提取一组时间序列的潜在成分，厘清其变异来源，并评估各频率下的相对相位或周期位置。该新方法为非参数方法，可应用于异相、高度非线性且频率和振幅均受调制的序列。我们证明了一个唯一性定理：在存在公共信息的情况下，无需拟合因子模型即可识别公共变异来源。该技术可应用于气候学、生物识别、工程、经济学等多个领域。我们通过一个振幅和频率均受调制的潜在信号合成示例，以及通过能源价格真实数据分析展示了M-CiSSA的性能，以了解主要一次能源大宗商品价格在不同频率下的主要驱动因素和联动关系，这对评估不同时间跨度的能源政策至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
