Multivariate Singular Spectrum Analysis: A General View and New Vector Forecasting Approach

Hassani, Hossein; Mahmoudvand, Rahim

doi:10.1142/s2335680413500051

Cited by 126 publications

(90 citation statements)

References 16 publications

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“…, y N ) where N is the length of the series. The SSA technique consists of two choices, the window length L and the number of eigenvalues r [25]. In SSA the number of components are related to the selection of the proper window length L which should be defined such that it minimises the signal distortion and maximises the residual noise level.…”

Section: Stage 1: Decompositionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

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Many scientific fields consider accurate and reliable forecasting methods as important decision-making tools in the modern age amidst increasing volatility and uncertainty. As such there exists an opportune demand for theoretical developments which can result in more accurate forecasts. Inspired by Colonial Theory, this paper seeks to bring about considerable improvements to the field of time series analysis and forecasting by identifying certain core characteristics of Colonial Theory which are subsequently exploited in introducing a novel approach for the grouping step of subspace based methods. The proposed algorithm shows promising results in terms of improved performances in noise filtering and forecasting of time series. The reliability and validity of the proposed algorithm is evaluated and compared with popular forecasting models with the results being thoroughly evaluated for statistical significance and thereby adding more confidence and value to the findings of this research.

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Section: Stage 1: Decompositionmentioning

confidence: 99%

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

Hassani

Ghodsi

Silva

et al. 2016

Digital Signal Processing

Self Cite

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“…Otherwise, if r is too large (overfitting), then a part of noise together with the signal will be approximated in the reconstructed series [56].…”

Section: Parameters Of the Ssa Algorithmmentioning

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 forecasting algorithm is based on SSA methodology [25][26][27]. In SSA terminology, it is often assumed that the series is noisy with an arbitrary series length N. The SSA technique consists of two main complementary stages: decomposition and reconstruction.…”

Section: Forecasting Model Based On the Singular Spectrum Analysismentioning

confidence: 99%

“…The window length represents a vector of L observations of the original series. If we remember Equation (3), we can see the window length model is similar to the k-th order model of the fuzzy time series, but taking into account original values from t = 1 to t = L. The usual value of L is (N + 1)/2 if N is odd and N/2 or (N/2) + 1 if N is even (for more details see [27]). The result of this step is the trajectory matrix:…”

Section: Forecasting Model Based On the Singular Spectrum Analysismentioning

confidence: 99%

A Hybrid Model for Forecasting Groundwater Levels Based on Fuzzy C-Mean Clustering and Singular Spectrum Analysis

Polomčić

Gligorić

Bajić

et al. 2017

Water

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Abstract:Having the ability to forecast groundwater levels is very significant because of their vital role in basic functions related to efficiency and the sustainability of water supplies. The uncertainty which dominates our understanding of the functioning of water supply systems is of great significance and arises as a consequence of the time-unbalanced water consumption rate and the deterioration of the recharge conditions of captured aquifers. The aim of this paper is to present a hybrid model based on fuzzy C-mean clustering and singular spectrum analysis to forecast the weekly values of the groundwater level of a groundwater source. This hybrid model demonstrates how the fuzzy C-mean can be used to transform the sequence of the observed data into a sequence of fuzzy states, serving as a basis for the forecasting of future states by singular spectrum analysis. In this way, the forecasting efficiency is improved, because we predict the interval rather than the crisp value where the level will be. It gives much more flexibility to the engineers when managing and planning sustainable water supplies. A model is tested by using the observed weekly time series of the groundwater source, located near the town ofČačak in south-western Serbia.

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Multivariate Singular Spectrum Analysis: A General View and New Vector Forecasting Approach

Cited by 126 publications

References 16 publications

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

From nature to maths: Improving forecasting performance in subspace-based methods using genetics Colonial Theory

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

A Hybrid Model for Forecasting Groundwater Levels Based on Fuzzy C-Mean Clustering and Singular Spectrum Analysis

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