The Synthesis of an Adaptive Filter for Automatic Detection and Time Localization of Premotor EEG Patterns of the Human Brain

An adaptive low-pass filter has been synthesized for automatic patterns detection of arbitrary mental movements in records of multidimensional electroencephalograms (EEG). The filter is based on the multivariate singular spectrum analysis. The effective bandwidth of the filter corresponds to the spectrum of the sought patterns of mental activity on the observed EEG time interval. The use of the synthesized filter provided a reliable automatic search for patterns and the correct determination of their time boundaries. The correctness of the results has been confirmed in experiments with 24 volunteers.

Gate Road Support Deformation Forecasting Based on Multivariate Singular Spectrum Analysis and Fuzzy Time Series

Underground mining engineers and planners in our country are faced with extremely difficult working conditions and a continuous shortage of money. Production disruptions are frequent and can sometimes last more than a week. During this time, gate road support is additionally exposed to rock stress and the result is its progressive deformation and the loss of functionality of gate roads. In such an environment, it is necessary to develop a low-cost methodology to maintain a gate road support system. For this purpose, we have developed a model consisting of two main phases. The first phase is related to support deformation monitoring, while the second phase is related to data analysis. To record support deformations over a defined time horizon we use laser scanning technology together with multivariate singular spectrum analysis to conduct data processing and forecasting. Fuzzy time series is applied to classify the intensity of displacements into several independent groups (clusters).

Forecasting tourist arrivals using multivariate singular spectrum analysis

The accurate forecasting of tourist arrivals has become a necessity for destination managers and tourism businesses. Singular spectrum analysis (SSA) has been applied in other areas, although its application in tourism demand is limited to SSA using a single univariate time series. New developments in the field extend the univariate framework into a multivariate SSA (MSSA). This article aims to forecast tourist arrivals from five continents to South Africa using MSSA and to compare the forecasting accuracy with that of univariate SSA as well as the baseline seasonal naïve model. The results show that in all but one case, MSSA leads to improved forecasting accuracy compared to univariate SSA and that these improvements are especially prevalent when forecasting over longer time horizons.