Evaluating the efficacy of singular spectrum transformation in detecting working posture changes in a time series

In this article, we focus on the coordinative structure of human behavior, which contributes to specifying dynamics from time-series kinematic data. We propose a method for the extraction of dynamical interaction from time-series data of human behavior using Singular Spectrum Transformation. Using the proposed method, human behavior can be described as a letter string whose letters indicate where the motion segmentation is detected. We also discuss a method of extracting coordinative structures by constructing multiple alignments from the timing structure of extracted motion change points. To confirm the effectivity of the proposed method, the results of motion analysis are shown.

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An extended singular spectrum transformation (SST) for the investigation of Kenyan precipitation data

Nonlinear Processes in Geophysics ◽

10.5194/npg-20-467-2013 ◽

2013 ◽

Vol 20 (4) ◽

pp. 467-481 ◽

Cited By ~ 4

Author(s):

N. Itoh ◽

N. Marwan

Keyword(s):

Time Series ◽

Singular Spectrum Analysis ◽

Change Point Detection ◽

Linear Functions ◽

Change Points ◽

Precipitation Data ◽

Real World Data ◽

Singular Spectrum ◽

Spectrum Transformation ◽

Point Detection

Abstract. In this paper a change-point detection method is proposed by extending the singular spectrum transformation (SST) developed as one of the capabilities of singular spectrum analysis (SSA). The method uncovers change points related with trends and periodicities. The potential of the proposed method is demonstrated by analysing simple model time series including linear functions and sine functions as well as real world data (precipitation data in Kenya). A statistical test of the results is proposed based on a Monte Carlo simulation with surrogate methods. As a result, the successful estimation of change points as inherent properties in the representative time series of both trend and harmonics is shown. With regards to the application, we find change points in the precipitation data of Kenyan towns (Nakuru, Naivasha, Narok, and Kisumu) which coincide with the variability of the Indian Ocean Dipole (IOD) suggesting its impact of extreme climate in East Africa.

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Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

Journal of Applied Geodesy ◽

10.1515/jag-2020-0005 ◽

2020 ◽

Vol 14 (3) ◽

pp. 295-302

Author(s):

Chuandong Zhu ◽

Wei Zhan ◽

Jinzhao Liu ◽

Ming Chen

Keyword(s):

Time Series ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Mixture Effect ◽

Singular Spectrum ◽

Grace Data ◽

Terrestrial Water ◽

Annual Signal ◽

Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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An Approach for Evolving Neuro-Fuzzy Forecasting of Time Series Based on Parallel Recursive Singular Spectrum Analysis

Fuzzy Sets and Systems ◽

10.1016/j.fss.2021.09.009 ◽

2021 ◽

Author(s):

Selmo Eduardo Rodrigues Júnior ◽

Ginalber Luiz de Oliveira Serra

Keyword(s):

Time Series ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Singular Spectrum ◽

Neuro Fuzzy

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Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

Fluctuation and Noise Letters ◽

10.1142/s0219477518500177 ◽

2018 ◽

Vol 17 (02) ◽

pp. 1850017 ◽

Cited By ~ 3

Author(s):

Mahdi Kalantari ◽

Masoud Yarmohammadi ◽

Hossein Hassani ◽

Emmanuel Sirimal Silva

Keyword(s):

Time Series ◽

Spectrum Analysis ◽

Missing Values ◽

Time Series Data ◽

Singular Spectrum Analysis ◽

Series Data ◽

L1 Norm ◽

Nonparametric Approach ◽

Singular Spectrum ◽

Simulated Time

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the [Formula: see text] norm-based version of Singular Spectrum Analysis (SSA), namely [Formula: see text]-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially [Formula: see text]-SSA can provide better imputation in comparison to other methods.

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