We present investigation of gene expression profiles by means of singular spectrum analysis (SSA). The biological problem under investigation is the decomposition of bicoid protein profiles of Drosophila melanogaster into the sum of a signal and noise, where the former consists of an exponential-in-distance pattern and is close to constant nonspecific component, or "background." The signal proc...

We propose an application of SVD model reduction to the class of RBF neural models for improving performance in contexts such as on-line prediction of time series. The SVD is coupled with QR-cp factorization. It has been found that such a coupling leads to more precise extraction of the relevant information, even when using it in an heuristic way. Singular Spectrum Analysis (SSA) and its relati...

Singular Spectrum Analysis-Linear Recurrent Formulae (SSA-LRF) is a forecasting method that starts by decomposing time series data into several independent and interpretable components. SSA-LRF does not have any assumptions must be fulfilled thus it more flexible to use. In this research, an empirical study of has trend pattern will carried out using without difference transformation with trans...

<span>The main objective of stock market investors is to maximize their gains. As a result, price forecasting has not lost interest in recent decades. Nevertheless, prices are influenced by news, rumor, and various economic factors. Moreover, the characteristics specific markets can differ significantly between countries regions, based on size, liquidity, regulations. Accordingly, it diff...

We present a technique for spatiotemporal data analysis called nonlinear Laplacian spectral analysis (NLSA), which generalizes singular spectrum analysis (SSA) to take into account the nonlinear manifold structure of complex data sets. The key principle underlying NLSA is that the functions used to represent temporal patterns should exhibit a degree of smoothness on the nonlinear data manifold ...

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme ...

Non-ferrous metals are important bulk commodities and play a significant part in the development of society. Their price forecast is great reference value for investors policymakers. However, developing robust model tricky due to price’s drastic fluctuations. In this work, novel fusion based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Singular Spectrum Analy...

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on existing methodology for removing artifacts, e.g., ocular, cardiac, muscle artifacts. The datasets, simulation platforms, performance measures artifact removal methods in previous related research are summa...

Novel coronavirus also known as COVID-19 was first discovered in Wuhan, China by end of 2019. Since then, the virus has claimed millions lives worldwide. In 29th April 2020, there were more than 5,000 outbreak cases Malaysia reported Ministry Health (MOHE). This study aims to evaluate trend analysis using Mann-Kendall test, and predict future Recurrent Forecasting-Singular Spectrum Analysis (RF...

Due to the spread of COVID-19 that hit Malaysia, all academic activities at educational institutions including universities had be carried out via online learning However, effectiveness is remains unanswered Besides, may have a significant impact if continued in upcoming sessions Therefore, core this study predict performance undergraduate students one public Malaysia by using Recurrent Forecas...