On the separability between signal and noise in singular spectrum analysis

The optimal value of the window length in singular spectrum analysis (SSA) is considered with respect to the concept of separability between signal and noise component, from the theoretical and practical perspective. The theoretical results confirm that for a wide class of time series of length N, the suitable value of this parameter is median{1, …, N}. The results of both simulated and real data verify the effectiveness of the theoretical results. The theoretical results obtained here coincide with those obtained previously from the empirical point of view.

[1]  Yasser F. O. Mohammad,et al.  Discovering causal change relationships between processes in complex systems , 2011, 2011 IEEE/SICE International Symposium on System Integration (SII).

[2]  Dimitrios D. Thomakos,et al.  A review on singular spectrum analysis for economic and financial time series , 2010 .

[3]  I. Hudson,et al.  Singular Spectrum Analysis: Climatic Niche Identification , 2010 .

[4]  Qiang Zhang,et al.  Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting , 2011 .

[5]  Tomoyuki Higuchi,et al.  Onset time determination of precursory events in time series data by an extension of Singular Spectrum Transformation , 2011 .

[6]  Rahim Mahmoudvand,et al.  FILTERING AND DENOISING IN LINEAR REGRESSION ANALYSIS , 2010 .

[7]  E. V. Gijo Demand forecasting of tea by seasonal ARIMA model , 2011 .

[8]  J. C. Cepeda,et al.  Dynamic vulnerability assessment due to transient instability based on data mining analysis for Smart Grid applications , 2011, 2011 IEEE PES CONFERENCE ON INNOVATIVE SMART GRID TECHNOLOGIES LATIN AMERICA (ISGT LA).

[9]  Saeid Sanei,et al.  The use of noise information for detection of temporomandibular disorder , 2009, Biomed. Signal Process. Control..

[10]  Christina Beneki,et al.  Signal Extraction and Forecasting of the UK Tourism Income Time Series. A Singular Spectrum Analysis Approach , 2012 .

[11]  A. Zhigljavsky,et al.  Forecasting European industrial production with singular spectrum analysis , 2009 .

[12]  Yin Zhang,et al.  Rapid detection of maintenance induced changes in service performance , 2011, CoNEXT '11.

[13]  Yubing Gong,et al.  NON-GAUSSIAN NOISE- AND COUPLING-INDUCED FIRING TRANSITIONS OF NEWMAN-WATTS NEURONAL NETWORKS , 2011 .

[14]  Tao Wang,et al.  Modeling daily realized futures volatility with singular spectrum analysis , 2002 .

[15]  J. Litniewski,et al.  Synthetic Aperture Technique Applied to Tissue Attenuation Imaging , 2011 .