Does noise reduction matter for curve fitting in growth curve models?

Hassani, Hossein; Zokaei, Mohammad; Rosen, Dietrich von; Amiri, Saeid; Ghodsi, Mansoureh

doi:10.1016/j.cmpb.2009.04.014

Cited by 21 publications

(14 citation statements)

References 22 publications

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“…Portanto, a identificação clássica do modelo PAR(p) fundamenta-se em determinar as ordens apropriadas aos operadores autorregressivos de cada período , . Estas ordens são determinadas de acordo com as estimativas , e substituindo as autocorrelações pelos respectivos valores amostrais em (11). Se a ordem do operador autorregressivo em um determinado período m for igual a , então terá distribuição aproximadamente normal com média zero e variância quando .…”

Section: Multi-channel Singular Spectrum Analysisunclassified

Geração De Cenários De Velocidade Do Vento Com Modelagem Autorregressiva Periódica Associada À Mssa

Menezes¹,

Cassiano²,

Souza³

et al. 2016

Anais Do XVIII Simpósio De Pesquisa Operacional &Amp; Logística Da Marinha

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ResumoSingular Spectrum Analysis (SSA) é uma técnica que decompõe uma série temporal em componentes tais como tendência, harmônicos e ruídos. Deixando de fora as componentes ruidosas e somando as outras, a série temporal pode ser suavizada. A geração de Cenários através do modelo autorregressivo periódico PAR(p) vem sendo amplamente utilizada em modelagem de séries de Energia Natural Afluente. Este artigo apresenta uma abordagem deste método de decomposição, na qual a modelagem PAR (p) é aplicada na série suavizada obtida pela SSA e por sua versão multivariada MSSA. Na ocasião são gerados 200 cenários de velocidade do vento para um horizonte de previsão de 60 meses. Para ilustrar a metodologia proposta, foram consideradas séries de velocidade do vento registradas em duas localidades na região Nordeste do Brasil. Os resultados obtidos mostram que tanto a modelagem PAR(p) quanto a geração de cenários são beneficiados pela suavização prévia da série temporal por SSA/MSSA. Palavras Chave: MSSA, modelagem PAR(p), velocidade do vento, geração de cenários. AbstractSingular spectrum analysis (SSA) is a technique that decomposes a time series into a set of components, such as, trend, harmonics, and residuals. Leaving out the residual components and adding up the others, the time series can be smoothed. The scenarios generations through PAR(p), periodic autoregressive models, has been broadly used in modeling Affluent natural Energy series. This article presents an approach of this decomposition method, in which the PAR (p) model is applied to the smoothed series obtained by SSA and its multivariate version, named MSSA. In this case, 200 scenarios are generated for a forecasting horizon of 60 months. To illustrate the application of the proposed methodology were considered wind speed time series recorded at two locations in northeastern Brazil. The results show that both the PAR (p) model and scenarios generations are favored by prior smoothing of the time series by SSA / MSSA.

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Section: Multi-channel Singular Spectrum Analysisunclassified

Geração De Cenários De Velocidade Do Vento Com Modelagem Autorregressiva Periódica Associada À Mssa

Menezes¹,

Cassiano²,

Souza³

et al. 2016

Anais Do XVIII Simpósio De Pesquisa Operacional &Amp; Logística Da Marinha

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“…Furthermore and unlike many other methods, the SSA works well even for small sample sizes making it possible to quickly update the coordinator rotation to varying signals block by block in relatively small blocks [26][27][28].…”

Section: Rationalementioning

confidence: 99%

“…noise and many other applications [34] and extracting the rhythms of the brain of electroencephalography [35]. The method has been employed and shown its capabilities for noise reduction for longitudinal measurements and surface roughness monitoring [26,30]. It has also been implemented for structural damage detection [36].…”

Section: Overviewmentioning

confidence: 99%

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Eldwaik

2018

Technologies

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Abstract:Wind induced noise is one of the major concerns of outdoor acoustic signal acquisition. It affects many field measurement and audio recording scenarios. Filtering such noise is known to be difficult due to its broadband and time varying nature. In this paper, a new method to mitigate wind induced noise in microphone signals is developed. Instead of applying filtering techniques, wind induced noise is statistically separated from wanted signals in a singular spectral subspace. The paper is presented in the context of handling microphone signals acquired outdoor for acoustic sensing and environmental noise monitoring or soundscapes sampling. The method includes two complementary stages, namely decomposition and reconstruction. The first stage decomposes mixed signals in eigen-subspaces, selects and groups the principal components according to their contributions to wind noise and wanted signals in the singular spectrum domain. The second stage reconstructs the signals in the time domain, resulting in the separation of wind noise and wanted signals. Results show that microphone wind noise is separable in the singular spectrum domain evidenced by the weighted correlation. The new method might be generalized to other outdoor sound acquisition applications.

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“…The theoretical underpinnings and practical background of the SSA technique were introduced by Golyandina et al in 2001 [18] and further by Hassani [19,20] and Viljoen for common time series [21]. The applications of this technique are many and various, for example, in meteorology, physics, economics, and financial mathematics.…”

Section: Ssamentioning

confidence: 99%

“…Furthermore, a pure noise series typically generates a gradually reducing sequence of singular values. It is noteworthy that there are alternate methods for the selection of r, refer to Hassani et al (2016) for instance [20].…”

Section: Selection Of Rmentioning

confidence: 99%

Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

2016

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During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to generate project time series data as daily progressive estimations in different project environments that could help researchers in generating general and customized formulae in further studies. This paper, however, is an attempt to introduce a new simulation approach to reflect the data regarding time series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. Moreover, this simulator can equip project managers with estimated information, which reassures them of the execution stages of the project although they lack historical data. A case study is presented to show the usefulness of the model and its applicability in practice. In this study, singular spectrum analysis has been employed to analyze the simulated outputs, and the results are separated based on their signal and noise trends. The signal trend is used as a point-of-reference to compare the outputs of a simulation employing S-curve technique results and the formulae corresponding to earned value management, as well as the life of a given project.

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Does noise reduction matter for curve fitting in growth curve models?

Cited by 21 publications

References 22 publications

Geração De Cenários De Velocidade Do Vento Com Modelagem Autorregressiva Periódica Associada À Mssa

Geração De Cenários De Velocidade Do Vento Com Modelagem Autorregressiva Periódica Associada À Mssa

Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

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