Circulant singular spectrum analysis: A new automated procedure for signal extraction

Bógalo, Juan; Poncela, Pilar; Senra, Eva

doi:10.1016/j.sigpro.2020.107824

Cited by 35 publications

(30 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its main advantage is that users can group the extracted components according to their needs because those components are precisely identified by frequency. CiSSA can be applied to stationary and non-stationary time series, as Theorem 2 in [18] states. However, for simplicity in presenting the method, let us assume that {x t } is a zero mean stationary time series of length T, given in the vector x = (x 1 , • • • , x T ) , and let L be a positive integer, called window length, such that L < T/2.…”

Section: Circulant Singular Spectrum Analysismentioning

confidence: 99%

“…However, for simplicity in presenting the method, let us assume that {x t } is a zero mean stationary time series of length T, given in the vector x = (x 1 , • • • , x T ) , and let L be a positive integer, called window length, such that L < T/2. The four steps of any SSA signal extraction procedure adapted to Circulant SSA, as described in [18], are as follows:…”

Section: Circulant Singular Spectrum Analysismentioning

confidence: 99%

“…[8,16] make partial comparisons of different alternatives. We add to this literature by analyzing how posterior published data affect the estimated signals by comparing two procedures: a parametric ARIMA-model-based approach widely used by statistical offices (Tramo-Seats, see for instance [17]) and a nonparametric procedure based on Singular Spectrum Analysis, SSA, recently introduced in [18]. Though revisions are inherent to any moment in time, they are crucial when there is a business cycle turning point or abrupt changes in the data generation process [19,20], like the ones currently witnessed due to COVID-19.…”

Section: Introductionmentioning

confidence: 99%

“…Circulant Singular Spectrum Analysis, CiSSA, is a non-parametric procedure that allows one to automatically decompose any economic time series into trend, seasonal and business cycle components [18]. CiSSA relies on traditional Singular Spectrum Analysis, Mathematics 2021, 9, 1169 2 of 17 SSA, a non-parametric procedure based on subspace algorithms [21].…”

Section: Introductionmentioning

confidence: 99%

“…A diagonal averaging procedure needed to reconstruct the time series in the final step is proposed by [28]. Very recently, [18] introduces CiSSA, linking the eigen decomposition to the frequency of the extracted signals and proving the asymptotic equivalence of the three variants (Basic, Toeplitz and Circulant SSA). SSA is widely applied across fields like biometrics, climatology, energy and volcanic activity, among others [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

2021

Self Cite

View full text Add to dashboard Cite

Real-time monitoring of the economy is based on activity indicators that show regular patterns such as trends, seasonality and business cycles. However, parametric and non-parametric methods for signal extraction produce revisions at the end of the sample, and the arrival of new data makes it difficult to assess the state of the economy. In this paper, we compare two signal extraction procedures: Circulant Singular Spectral Analysis, CiSSA, a non-parametric technique in which we can extract components associated with desired frequencies, and a parametric method based on ARIMA modelling. Through a set of simulations, we show that the magnitude of the revisions produced by CiSSA converges to zero quicker, and it is smaller than that of the alternative procedure.

show abstract

Section: Circulant Singular Spectrum Analysismentioning

confidence: 99%

Section: Circulant Singular Spectrum Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

2021

Self Cite

View full text Add to dashboard Cite

show abstract

Singular spectrum analysis for value at risk in stochastic volatility models

Arteche

García‐Enríquez

2021

Journal of Forecasting

View full text Add to dashboard Cite

Estimation of the value at risk (VaR) requires prediction of the future volatility. Whereas this is a simple task in ARCH and related models, it becomes much more complicated in stochastic volatility (SV) processes where the volatility is a function of a latent variable that is not observable. In-sample (present and past values) and out-of-sample (future values) predictions of that unobservable variable are thus necessary. This paper proposes singular spectrum analysis (SSA), which is a fully nonparametric technique that can be used for both purposes. A combination of traditional forecasting techniques and SSA is also considered to estimate the VaR. Their performance is assessed in an extensive Monte Carlo and with an application to a daily series of S&P500 returns.

show abstract

Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm

Wang

et al. 2022

Journal of Forecasting

View full text Add to dashboard Cite

This research presents a hybrid model for multi-step, interval forecasting of air quality indices. An efficient preprocessing module is applied to split the raw data into various sub-series, and the optimal mode of data input is determined through feature selection. A multi-objective optimization algorithm is proposed to tune the parameters of kernel extreme learning machine to achieve high accuracy and stability. An evaluation with several error criteria, benchmark models, and critique is conducted using three daily air quality index datasets from three cities of China. Empirical results indicate that the developed model achieves superior predictions of air quality indices, which may be useful in policies for mitigating air pollution.

show abstract

Circulant singular spectrum analysis: A new automated procedure for signal extraction

Cited by 35 publications

References 49 publications

Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

Circulant Singular Spectrum Analysis to Monitor the State of the Economy in Real Time

Singular spectrum analysis for value at risk in stochastic volatility models

Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm

Contact Info

Product

Resources

About