A glance at the applications of Singular Spectrum Analysis in gene expression data

Hassani, Hossein; Ghodsi, Zara

doi:10.1016/j.bdq.2015.04.001

Cited by 13 publications

(8 citation statements)

References 42 publications

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“…Since numerous specific component grouping methods were separately proposed for SSA to reconstruct sinusoids [23], [24], trends [25] or noise [6], [9], all of them suffer of a lack of flexibility and require to be manually adapted to the analyzed signal to provide meaningful results. More recently, efforts aim at making the SSA fully automatic [26]- [28] and adaptive for any component type.…”

Section: B Automatic Grouping Of Componentsmentioning

confidence: 99%

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The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool

Harmouche

Fourer

Auger

et al. 2018

IEEE Trans. Signal Process.

109

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Singular Spectrum Analysis (SSA) is a signal decomposition technique that aims at expanding signals into interpretable and physically meaningful components (e.g. sinusoids, noise, etc.). This article presents new theoretical and practical results about the separability of the SSA and introduces a new method called sliding SSA. First, the SSA is combined with an unsupervised classification algorithm to provide a fully automatic data-driven component extraction method for which we investigate the limitations for components separation in a theoretical study. Second, the detailed automatic SSA method is used to design an approach based on a sliding analysis window which provides better results than the classical SSA method when analyzing non-stationary signals with a time-varying number of components. Finally, the proposed sliding SSA method is compared to the Empirical Mode Decomposition (EMD) and to the synchrosqueezed Short-Time Fourier Transform (STFT), applied on both synthetic and real-world signals.

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Section: B Automatic Grouping Of Componentsmentioning

confidence: 99%

“…with < x, y > = x T y and ||x|| = √ < x, x > (6) but can also be replaced by the weighted correlation (wcorrelation) as defined in [33]. This algorithm provides a binary tree that can be called a dendrogram, which shows at each iteration the result of the merging process.…”

Section: B Automatic Grouping Of Componentsmentioning

confidence: 99%

The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool

Harmouche

Fourer

Auger

et al. 2018

IEEE Trans. Signal Process.

109

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“…SSA, as a time series analysis tool used to decompose an original time sequence into interpretable components, has been applied in many fields, including biology, physics, climatology, and economics [35][36][37][38]. The decomposition process can be divided into four steps:…”

Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

et al. 2019

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Wind speed forecasting helps to increase the efficacy of wind farms and prompts the comparative superiority of wind energy in the global electricity system. Many wind speed forecasting theories have been widely applied to forecast wind speed, which is nonlinear, and unstable. Current forecasting strategies can be applied to various wind speed time series. However, some models neglect the prerequisite of data preprocessing and the objective of simultaneously optimizing accuracy and stability, which results in poor forecast. In this research, we developed a combined wind speed forecasting strategy that includes several components: data pretreatment, optimization, forecasting, and assessment. The developed system remedies some deficiencies in traditional single models and markedly enhances wind speed forecasting performance. To evaluate the performance of this combined strategy, 10-min wind speed sequences gathered from large wind farms in Shandong province in China were adopted as a case study. The simulation results show that the forecasting ability of our proposed combined strategy surpasses the other selected comparable models to some extent. Thus, the model can provide reliable support for wind power generation scheduling.

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“…In recent years, singular spectrum analysis (SSA), a relatively novel and powerful nonparametric technique in time series analysis, has been developed and applied to many practical problems across different fields. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] The SSA algorithm incorporates elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing, 16,17 and can be used for: smoothing, trend extraction, extraction of periodicities, forecasting, filling in missing values, estimating signal parameters, detection of change points, and finding causality between series. Being a nonparametric approach, although some probabilistic and statistical concepts are employed in the SSA algorithm, no statistical assumptions such as stationarity of the series or normality of the residuals are required.…”

Section: Introductionmentioning

confidence: 99%

Prediction intervals for the vector singular spectrum analysis forecasting algorithm in a median‐based singular spectrum analysis

Mahmoudvand

Rodrigues

2020

Comp and Math Methods

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In recent years singular spectrum analysis (SSA) has been used as a powerful technique to analyze time series, including theoretical developments and application to many practical problems. However, no inclusive theoretical approach has been discussed regarding the construction of confidence intervals for forecasts. Due to the prominent role of prediction intervals in evaluating the accuracy of forecasts in time series analysis, in this paper, we consider the topic of constructing prediction intervals for SSA. Namely, we revise the existing approaches for the vector SSA forecasting method and propose a new median‐based alternative to this algorithm where the mean in the diagonal averaging step of the SSA algorithm is replaced by the median. The results from the existing and proposed approaches are compared by considering Monte Carlo simulations and real data applications.

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A glance at the applications of Singular Spectrum Analysis in gene expression data

Cited by 13 publications

References 42 publications

The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool

The Sliding Singular Spectrum Analysis: A Data-Driven Nonstationary Signal Decomposition Tool

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

Prediction intervals for the vector singular spectrum analysis forecasting algorithm in a median‐based singular spectrum analysis

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