We introduce Multivariate Circulant Singular Spectrum Analysis (M-CiSSA) , a non-parametric automated technique to extract signals at different frequencies. It is useful to uncover co-movements per frequency, understand their commonalities and specificities, and also the relative cyclical position of the signals . We apply M-CiSSA to understand the main drivers and co-movements of energy commodity prices at trend and cyclical frequencies that are key to assess energy policy at different time horizons. We distinguish the detached behaviour of US natural gas from other energy commodities at both frequencies , while coal and Japan's natural gas only decouple at the cyclical frequency .
We introduce Multivariate Circulant Singular Spectrum Analysis (M-CiSSA) to solve in a unified way a problem of primary interest for data scientists: to extract 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 not on phase, highly nonlinear and modulated both in frequency and amplitude. We prove a uniqueness theorem that in case of common information and without the need of fitting a factor model, allows us to identify common sources of variation . We apply M-CiSSA to understand the main drivers and co-movements of energy commodity prices at various frequencies that are key to assess energy policy at different time horizons. Within the application we are able to denoise the latent signals by frequency. As a result, we also distinguish the detached behaviour of US natural gas from other energy commodities at the long and medium run , while coal and Japan's natural gas only decouple in the medium term .
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to solve in a unified way a problem of primary interest for data scientists: to extract 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
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automated technique to extract signals at different frequencies. It is useful to uncover co-movements per frequency, understand their commonalities and specificities, and also the relative cyclical position of the signals
and can be applied to series not on phase, highly nonlinear and modulated both in frequency and amplitude. We prove a uniqueness theorem that in case of common information and without the need of fitting a factor model, allows us to identify common sources of variation
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trend and cyclical
various
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We
Within the application we are able to denoise the latent signals by frequency. As a result, we also
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both frequencies
the long and medium run
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at the cyclical frequency
in the medium term