On the Separability Between Signal and Noise in Singular Spectrum Analysis

Hassani, Hossein; Mahmoudvand, Rahim; Zokaei, Mohammad; Ghodsi, Mansoureh

doi:10.1142/s0219477512500149

Cited by 43 publications

(16 citation statements)

References 18 publications

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“…results and studies based on a wide class of simulated and real data which indicate that the optimal choice of the embedding dimension or window length is close to one half of the time series length [15,16]. The number of noise surrogates used in MCSSA was 1000.…”

Section: Resultsmentioning

confidence: 99%

Use of SSA and MCSSA in the Analysis of Cardiac RR Time Series

Thuraisingham¹

2013

Journal of Computational Medicine

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A new preprocessing procedure in the analysis of cardiac RR interval time series is described. It uses the singular spectrum analysis (SSA) and the Monte Carlo SSA (MCSSA) test. A novel feature of this preprocessing procedure is the ability to identify the noise component present in the series with a given probability and to separate the time series into a trend, signal, and noise. The MCSSA test involves testing whether the modes obtained from SSA can be generated by a noise process leading to separation of the noise modes from the signal. The procedure described here does not discard or modify any sample in the record but merely separates the time series into a trend, signal, and noise, allowing for further analysis of these components. The procedure is not limited to the length of the record and could be applied to nonstationary data. The basis functions used in SSA are data adaptive in that they are not chosen a priori but instead are dependent on the data set used, increasing flexibility to the analysis. The procedure is illustrated using the RR interval time series of a healthy, congestive heart failure, and atrial fibrillation subject.

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Section: Resultsmentioning

confidence: 99%

Use of SSA and MCSSA in the Analysis of Cardiac RR Time Series

Thuraisingham¹

2013

Journal of Computational Medicine

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“…Based on the behavior of the singular values and the w-correlations (a definition of the w-correlation can be found in Golyandina et al, 2001) among components within each dataset, we consider r = 13, r = 6, and r = 17, for the USAc-cDeaths, UKgas and AirPassengers, respectively. It is worth mentioning that USAccDeaths series was previously studied in Hassani (2007) and Hassani et al (2012), and they found that the above parameters are optimal in the sense that they produce acceptable forecasts.…”

Section: Real Datamentioning

confidence: 98%

A new parsimonious recurrent forecasting model in singular spectrum analysis

Mahmoudvand

Rodrigues

2017

Journal of Forecasting

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Singular spectrum analysis (SSA) is a powerful nonparametric method in the area of time series analysis that has shown its capability in different applications areas. SSA depends on two main choices: the window length L and the number of eigentriples used for grouping r. One of the most important issues when analyzing time series is the forecast of new observations. When using SSA for time series forecasting there are several alternative algorithms, the most widely used being the recurrent forecasting model, which assumes that a given observation can be written as a linear combination of the L−1 previous observations. However, when the window length L is large, the forecasting model is unlikely to be parsimonious. In this paper we propose a new parsimonious recurrent forecasting model that uses an optimal m(

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“…As explained in Hassani et al (2012), if the absolute value of the wcorrelations is small, then the corresponding series are almost w-orthogonal, but, if it is large, then the two series are far from being w-orthogonal and are therefore badly separable. As an example, we present in Figure 2 the wcorrelation matrix for the United States to show the dependence between signal and noise components.…”

Section: Evaluating the Mfmssa Approachmentioning

confidence: 99%

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

Hassani

Rua

Silva

et al. 2019

International Journal of Forecasting

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The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) in predicting/fitting quarterly variables (e.g. real GDP). In this paper we propose a Multivariate Singular Spectrum Analysis (MSSA) based method for mixed frequency interpolation and forecasting, which can be used for any mixed frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed frequency modelling and forecasting, and useful extensions of this method, are also discussed. JEL: C18, C53, E17

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On the Separability Between Signal and Noise in Singular Spectrum Analysis

Cited by 43 publications

References 18 publications

Use of SSA and MCSSA in the Analysis of Cardiac RR Time Series

Use of SSA and MCSSA in the Analysis of Cardiac RR Time Series

A new parsimonious recurrent forecasting model in singular spectrum analysis

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

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