Dimension reduction techniques for multivariate time series decompose the observed into a few useful independent/orthogonal univariate components. A spectral domain method is developed second-order stationary that linearly transforms several groups of lower-dimensional subseries. These subseries have non-zero coherence among components within group but zero across groups. The expressed as sum f...

The new coronavirus (COVID-19) has spread to over 200 countries, with 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable forecasting the epidemic worldwide have been produced. This paper reviews and summarizes most relevant for COVID-19. dataset is derived from world health organization (WHO) COVID-19 dashboard, it contains official daily co...

In this work, we aim to provide a new and efficient recursive detection method for temporarily monitored signals. Motivated by the case of propagation an event over field sensors, assumed that change in statistical properties signals can only be temporary. Unfortunately, our best knowledge, existing simple techniques such as ones based on cumulative sum (CUSUM) do not consider temporary aspect ...

Abstract Recent network intrusion detection systems have employed machine learning and deep algorithms to defend against dynamically evolving attacks. While most previous studies focused on detecting attacks which can be determined based a single time instant, few paid attention subsequence outliers, require inspecting consecutive points in for detection. To address this issue, paper applies ti...