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Hassani H.,Bournemouth University | Hassani H.,Institute for International Energy Studies | Heravi S.,University of Cardiff | Zhigljavsky A.,University of Cardiff
Journal of Forecasting | Year: 2013

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models.We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. © 2012 John Wiley & Sons, Ltd. Source


Kalantari M.,Payame Noor University | Yarmohammadi M.,Payame Noor University | Hassani H.,Institute for International Energy Studies
Fluctuation and Noise Letters | Year: 2016

In recent years, the singular spectrum analysis (SSA) technique has been further developed and increasingly applied to solve many practical problems. The aim of this research is to introduce a new version of SSA based on L1-norm. The performance of the proposed approach is assessed by applying it to various real and simulated time series, especially with outliers. The results are compared with those obtained using the basic version of SSA which is based on the Frobenius norm or L2-norm. Different criteria are also examined including reconstruction errors and forecasting performances. The theoretical and empirical results confirm that SSA based on L1-norm can provide better reconstruction and forecasts in comparison to basic SSA when faced with time series which are polluted by outliers. © 2016 World Scientific Publishing Company. Source


Rahmatpour A.,Research Institute of Petroleum Industry RIPI | Vakili A.,Institute for International Energy Studies | Azizian S.,Research Institute of Petroleum Industry RIPI
Heteroatom Chemistry | Year: 2013

Polystyrene-supported gallium trichloride (PS/GaCl3) as a highly active and reusable heterogeneous Lewis acid effectively activates hexamethyldisilazane (HMDS) for the efficient silylation of alcohols and phenols at room temperature. In this heterogeneous catalytic system, primary, secondary, and tertiary alcohols as well as phenols were converted to their corresponding trimethylsilyl ethers with short reaction times and high yields under mild reaction conditions. The heterogenized catalyst is of high reusability and stability in the silylation reactions and was recovered several times with negligible loss in its activity or a negligible catalyst leaching, and also there is no need for regeneration. It is noteworthy that this method can be used for chemoselective silylation of different alcohols and phenols with high yields. © 2013 Wiley Periodicals, Inc. Source


Iranmanesh S.H.,University of Tehran | Miranian A.,Institute for International Energy Studies | Abdollahzade M.,University of Tehran
2012 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012 | Year: 2012

System identification is defined as finding mathematical models of systems, using experimental measurements and observations. This paper proposes an identification approach based on the singular spectrum analysis (SSA) and least squares support vector machines (LS-SVM) model. The SSA is used in the pre-processing stage for de-noising the measurement data and then the LS-SVM model is trained by the de-noised data. The proposed approach was employed for identification of two nonlinear systems. The simulation results demonstrated the promising performance of the proposed approach and favorable capabilities of the SSA for nonlinear system identification. © 2012 IEEE. Source


Ghodsi M.,Bournemouth University | Ghodsi M.,Institute for International Energy Studies | Yarmohammadi M.,Payame Noor University
Journal of Systems Science and Complexity | Year: 2014

Forecasting exchange rate is undoubtedly an attractive and challenging issue that has been of interest in different domains for many years. The singular spectrum analysis (SSA) technique has been used as a promising technique for time series forecasting including exchange rate series. The SSA technique is based upon two main choices: Window length, L, and the number of singular values, r. These values are very important for the reconstruction stage and forecasting purposes. Here the authors consider an optimum version of the SSA technique for forecasting exchange rates. The forecasting performances of the SSA technique for one-step-ahead forecast of six exchange rate series are used to find the best L and r. © 2014 Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag Berlin Heidelberg. Source

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