In order to extract any ULF signature associated with earthquakes, the principal component analysis (PCA) and singular spectral analysis (SSA) have been performed to investigate the possibility of discrimination of signals from different sources (geomagnetic variation, artificial noise, and the other sources (earthquake-related ULF emissions)). We adopt PCA to the time series data observed at c...

Transportation was an important sector of supporting the economic growth a country. The impact Covid-19 2020 pandemic at Achmad Yani International Airport in Semarang resulted movement number passengers decreasing quite drastically, but mid-2020 had slowly increased. Forecasting done to determine flow future using Hybrid Singular Spectrum Analysis (SSA)-Neural Network (NN) method. SSA method ex...

Among the smart factory studies, we describe defect detection research conducted on bearings, which are elements of mechanical facilities. Bearing has been consistently in past; however, most limited to using existing artificial intelligence models. In addition, previous studies assumed factories situated bearing were insufficient. Therefore, a recent was that applied an model and environment. ...

This study uses two different time series forecasting approaches (parametric and non-parametric) to assess a frequency magnitude of earthquakes above Mw 4.0 in Northeastern Algeria. The Autoregressive Integrated Moving Average (ARIMA) model encompasses the parametric approach, while non-parametric method employs Singular Spectrum Analysis (SSA) approach. ARIMA SSA models were then used train fo...

A climatic analysis of temperature and precipitation ®elds in the Sardinia region is developed. The main purpose of this work is the characterization of temperature and precipitation ®elds to better understand Sardinia's climate. Principal component analysis (PCA) and singular spectrum analysis (SSA) have been applied to maximum and minimum temperature and to cumulative daily precipitation data...

In the present paper we investigate methods related to both the Singular Spectrum Analysis (SSA) and subspacebased methods in signal processing. We describe common and specific features of these methods and consider different kinds of problems solved by them such as signal reconstruction, forecasting and parameter estimation. General recommendations on the choice of parameters to obtain minimal...

Low-frequency p-modes have relatively small amplitudes and are set against the background of granula-tion and active regions on the solar surface. We apply a number of time series analysis techniques to separate these modes from trends and the background. The calibrated signals of both the MDI and GOLF instruments are processed in the following steps: 1) whitening and gap lling; 2) signal extra...

High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested applicability Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) slow (controlled structural biochemical properties) from PR...

Intrusion detection systems typically create a large volume of alarms and most of them are false alarms that can be seen as background noises caused by normal system behaviors. Manual analysis of a large number of alarms is both time consuming and labor intensive. This study focuses on the statistical analysis of the alarm flow. Using the Singular Spectrum Analysis (SSA) approach, we found that...

Circadian rhythms become less dominant and less regular with chronic-degenerative disease, such that to accurately assess these pathological conditions it is important to quantify not only periodic characteristics but also more irregular aspects of the corresponding time series. Novel data-adaptive techniques, such as singular spectrum analysis (SSA), allow for the decomposition of experimental...