Nowadays, it is a common practice to protect various types of statistical data before publishing them for different researches. For instance, when conducting extensive demographic surveys such as national census, the collected data should be at least depersonalized to guarantee proper level of privacy preservation. In practice, even more complicated methods of data protection need to be used. A...

This article presents a novel methodology to assess the financial risk crops in highly weather-volatile regions. We use data-driven methodologies that singular value decomposition techniques low-income economy. The measure is first derived by applying frameworks, Principal Component Analysis (PCA), and Singular Spectrum (SSA) productive coffee Colombia (163 weather stations) during 2010–2019. o...

Abstract The popularity of smart home equipment has led to higher requirements for automation operation and maintenance. However, the energy consumption status hidden faults household cannot be controlled in time only by using traditional monitoring methods. Therefore, this paper proposes a methods power analysis appliances based on SSA-TCN data appliances. effective information is extracted th...

Swarm intelligence (SI) is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS) as well as the singular spectrum anal...

The majority of data sets in the geosciences are obtained from observations and measurements of natural systems, rather than in the laboratory. These data sets are often full of gaps, due to to the conditions under which the measurements are made. Missing data give rise to various problems, for example in spectral estimation or in specifying boundary conditions for numerical models. Here we use...

Singular Spectrum Analysis (SSA) is a powerful data decomposition/recompostion technique that can be used to reduce the noise in time series. Compared to existing solutions aiming at similar purposes, such as frequency-based filtering, it benefits from easier-to-exploit intuitions, applicability in contexts where low sampling rates make standard frequency analyses challenging, and the (theoreti...

This paper is aimed at introducing the powerful, nonparametric time series analysis and forecasting technique of Singular Spectrum Analysis (SSA) for trade forecasting via an application which evaluates the impact of the 2008 recession on U.S. trade forecasting models. This research is felicitous given the magnitude of the structural break visible in the U.S. trade series following the 2008 eco...

One of the major issues about operation power systems is prediction load demand. Moreover, forecasting prime concern to system operators. Recently, integration elements, such as renewable energy sources, storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in cycle. This study concentrates on short-term demand proposes a hybrid method that com...

Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series of length N whose intermittency can give rise to the divergence of their variance. The wavelet transform is a kind of local Fourier transform within a finite moving window whose width W , proportional to the major period of interest, is varied to explore a broad ra...

A wealth of epidemiological data suggests an association between mortality/morbidity from pulmonary and cardiovascular adverse events and air pollution, but uncertainty remains as to the extent implied by those associations although the abundance of the data. In this paper we describe an SSA (Singular Spectrum Analysis) based approach in order to decompose the time-series of particulate matter ...