The modification of the singular spectral analysis basic method for improving the accuracy of forecasting the irregularity of the Earth rotation

Растущие потребности в точности координатно-временных определений со стороны многих прикладных наук о Земле и практических задач приводят к необходимости постоянного совершенствования средств и методов определения и прогнозирования параметров вращения Земли. Параметр “всемирное время”, характеризующий фазу вращения Земли, в наибольшей степени среди других влияет на точность координатно-временных определений. В данной статье приводится описание применения метода сингулярного спектрального анализа к прогнозированию временных рядов параметров вращения Земли. Предлагается модификация базового метода, направленная на повышение точности прогноза. Выполнены сравнительные оценки точности прогнозов всемирного времени, рассчитанных методом сингулярного спектрального анализа, с аналогичными прогнозами Международной службы вращения Земли. Показана целесообразность применения метода сингулярного спектрального анализа для прогнозирования на интервалы более 50 дней Growing demand for accuracy of coordinate-time determinations from both many applied Earth sciences and practical problems requires the continuous improvement of means and methods for determining and predicting the parameters of the Earth rotation. Parameter “World time” characterizes phase of the Earth’s rotation and mostly affects the accuracy of coordinate-time determinations. This article describes application of method of singular spectral analysis for forecasting the time series of the Earth’s rotation parameters. We propose modification of basic method, which aims at increasing forecast accuracy. We made comparative estimates for accuracy of world time forecasts calculated by the method of singular spectral analysis with similar forecasts by the International Earth Rotation Service. The expediency of using the method of singular spectral analysis for predicting intervals of more than 50 days is shown

FORECASTING FINANCIAL CYCLES: CAN BIG DATA HELP?

Financial cycles as a source of financial crisis and business cycles that was demonstrated during the financial crisis of 2008, so it is important to understand proper methods of measuring and forecasting them to unravel their true nature. We searched financial big data for the UK, USA, Japan and China for a period 2004Q1 to 2019Q1 to find important data corresponding to the research and determine their importance for the financial cycle studies. We use singular spectral analysis (SSA without financial big data) and multichannel singular spectral analysis (MSSA with financial big data) to identify significant deterministic cycles in the residential property prices, credits to private non-financial sector and credit share in the GDP. The forecast test results show on the data for the UK, USA, Japan and China that inclusion of the financial big data significantly (on the level from 30% to four times) improves forecast accuracy for financial cycle components. This is a first study on the importance of the link between financial cycles and financial big data. Policymakers, practitioners and financial cycles research should take into the account the importance of financial big data for the studies of financial cycles for a better understanding of their true nature and improving their forecast accuracy.

Electromagnetic Testing of Moisture Separation Reheater Tube based on Multivariate Singular Spectral Analysis

Moisture separator reheater (MSR) tubing systems are an important part of a pressurized-water power plant to increase the efficiency of the heat transfer rate. The MSR tubes are finned tubes which are made of ferritic stainless steel (SS439) with a high strength and corrosion resistance characteristics. However, corrosion can appear along with the fins after a long period of operation of the MSR tubes that requires nondestructive testing (NDT) of the MSR tubes’ periodically. Electromagnetic testing (ET) is an efficient NDT method for the inspection of far-side corrosion in the MSR tubes. However, the ET sensor signal is affected by signal noise from the fins. Material degradation that make it challenging to inspect and evaluate the corrosion. In this study, we proposed three ET methods, including magnetic flux leakage testing, eddy current testing and partial saturation eddy current testing, and incorporated with a multivariate singular spectral analysis (MSSA) filter to improve the detectability of the corrosion in the MSR tubes. The proposed MSSA filter was compared with the multivariate wavelet transform filter and Gabor transform filter, and the results showed more efficient and stable results of the MSSA filter in the extraction of the corrosion signal.