arXiv subjects

Eva Senra

Publications and source records attributed to Eva Senra.

3 recordsLinked to original sources

arXiv2021

cissa(): A MATLAB Function for Signal Extraction

cissa() is a MATLAB function for signal extraction by Circulant Singular Spectrum Analysis, a procedure proposed in Bogalo et al (2021). cissa() extracts the underlying signals in a time series identifying their frequency of oscillation in an automated way, by just introducing the data and the window length. This solution can be applied to stationary as well as to non-stationary and non-linear time series. Additionally, in this paper, we solve some technical issues regarding the beginning and end of sample data points. We also introduce novel criteria in order to reconstruct the underlying signals grouping some of the extracted components. The output of cissa() is the input of the function group() to reconstruct the desired signals by further grouping the extracted components. group() allows a novel user to create standard signals by automated grouping options while an expert user can decide on the number of groups and their composition. To illustrate its versatility and performance in several fields we include 3 examples: an AM-FM synthetic signal, an example of the physical world given by a voiced speech signal and an economic time series. Possible applications include de-noising, de-seasonalizing, de-trending and extracting business cycles, among others.

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arXiv2020

Understanding fluctuations through Multivariate Circulant Singular Spectrum Analysis

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.

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arXiv2020

Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction

Sometimes, it is of interest to single out the fluctuations associated to a given frequency. We propose a new variant of SSA, Circulant SSA (CiSSA), that allows to extract the signal associated to any frequency specified beforehand. This is a novelty when compared with other procedures that need to identify ex-post the frequencies associated to extracted signals. We prove that CiSSA is asymptotically equivalent to these alternative procedures although with the advantage of avoiding the need of the subsequent frequency identification. We check its good performance and compare it to alternative SSA methods through several simulations for linear and nonlinear time series. We also prove its validity in the nonstationary case. To show how it works with real data, we apply CiSSA to extract the business cycle and deseasonalize the Industrial Production Index of six countries. Economists follow this indicator in order to assess the state of the economy in real time. We find that the estimated cycles match the dated recessions from the OECD showing its reliability for business cycle analysis. Finally, we analyze the strong separability of the estimated components. In particular, we check that the deseasonalized time series do not show any evidence of residual seasonality.

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