On multiplicative seasonal modelling for vector time series

Ursu, Eugen; Duchesne, Pierre

doi:10.1016/j.spl.2009.06.017

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Despite the popularity of the SARMA models in various economic time series and financial data, the portmanteau tests at seasonal lags where s is the seasonal period, has not yet received as much attention as it should deserve. Recently Duchesne ( 2007 ), Ursu and Duchesne ( 2009 ) considered serial correlation testing in multiplicative seasonal univariate and multivariate time series models. Duchesne ( 2007 ) proposed his test statistic based on a kernel-based spectral density estimator of Shin ( 2004 ), whose weighting scheme is more adapted to autocorrelations associated to seasonal lags.…”

Section: Introductionmentioning

confidence: 99%

Portmanteau test statistics for seasonal serial correlation in time series models

Mahdi

2016

SpringerPlus

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The seasonal autoregressive moving average SARMA models have been widely adopted for modeling many time series encountered in economic, hydrology, meteorological, and environmental studies which exhibited strong seasonal behavior with a period s. If the model is adequate, the autocorrelations in the errors at the seasonal and the nonseasonal lags will be zero. Despite the popularity uses of the portmanteau tests for the SARMA models, the diagnostic checking at the seasonal lags , where m is the largest lag considered for autocorrelation and s is the seasonal period, has not yet received as much attention as it deserves. In this paper, we devise seasonal portmanteau test statistics to test whether the seasonal autocorrelations at multiple lags s of time series are different from zero. Simulation studies are performed to assess the performance of the asymptotic distribution results of the proposed statistics in finite samples. Results suggest to use the proposed tests as complementary to those classical tests found in literature. An illustrative application is given to demonstrate the usefulness of this test.

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Section: Introductionmentioning

confidence: 99%

Portmanteau test statistics for seasonal serial correlation in time series models

Mahdi

2016

SpringerPlus

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Multivariate portmanteau tests for weak multiplicative seasonal VARMA models

Amir

Maïnassara

2018

Stat Papers

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The Hadamard decomposition problem

Ciaperoni,

Gionis,

Mannila

2024

Data Min Knowl Disc

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We introduce the Hadamard decomposition problem in the context of data analysis. The problem is to represent exactly or approximately a given matrix as the Hadamard (or element-wise) product of two or more low-rank matrices. The motivation for this problem comes from situations where the input matrix has a multiplicative structure. The Hadamard decomposition has potential for giving more succint but equally accurate representations of matrices when compared with the gold-standard of singular value decomposition (svd). Namely, the Hadamard product of two rank-$$h$$ h matrices can have rank as high as $${h}^2$$ h 2 . We study the computational properties of the Hadamard decomposition problem and give gradient-based algorithms for solving it approximately. We also introduce a mixed model that combines svd and Hadamard decomposition. We present extensive empirical results comparing the approximation accuracy of the Hadamard decomposition with that of the svd using the same number of basis vectors. The results demonstrate that the Hadamard decomposition is competitive with the svd and, for some datasets, it yields a clearly higher approximation accuracy, indicating the presence of multiplicative structure in the data.

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On multiplicative seasonal modelling for vector time series

Cited by 3 publications

References 28 publications

Portmanteau test statistics for seasonal serial correlation in time series models

Portmanteau test statistics for seasonal serial correlation in time series models

Multivariate portmanteau tests for weak multiplicative seasonal VARMA models

The Hadamard decomposition problem

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