Indonesia is known for its excessive rainfall. Rainfall trends in an area have different characteristics. Differences latitude, apparent motion of the sun, geographical position, topography, and interaction many forms air circulation all contribute to this. time series essential engineering planning, particularly water infrastructure like irrigation, dams, urban drainage, ports, wharves. Althou...

In this paper, unique approach is presented for the electroencephalography (EEG) signals analysis. This is based on Eigen values distribution of a matrix which is called as scaled Hankel matrix. This gives us a way to find out the number of Eigen values essential for noise reduction and extraction of signal in singular spectrum analysis. This paper gives us an approach to classify the EEG signa...

the record of human brain neural activities, namely electroencephalogram (eeg), is generally known as a non-stationary and nonlinear signal. in many applications, it is useful to divide the eegs into segments within which the signals can be considered stationary. combination of empirical mode decomposition (emd) and hilbert transform, called hilbert-huang transform (hht), is a new and powerful ...

This paper uses univariate and multivariate singular spectrum analysis for predicting the value and the direction of changes in the daily pound / dollar exchange rate. To perform the forecast, we also use the daily dollar exchange rates with respect to Euro and Japanese yen. We use the random walk model as a benchmark model to evaluate performances of the singular spectrum analysis as a predict...

Singular spectrum analysis (SSA), a linear univariate and multivariate time series technique , is essentially principal component analysis (PCA) applied to the time series and additional copies of the time series lagged by 1 to K time steps. Neural network theory has meanwhile allowed PCA to be generalized to nonlinear PCA (NLPCA). In this paper, NLPCA is further extended to perform nonlinear S...

Singular spectrum analysis (SSA) is a method of time-series analysis based on the singular value decomposition of an associated Hankel matrix. We present an approach to SSA using an effective and numerically stable high-degree polynomial approximation of a spectral projector, which also provides a means of time-series forecasting. Several numerical examples illustrating the algorithm are given.

We propose a novel approach for constructing Singular Spectrum Analysis (SSA) forecast based on estimating new coefficients of Linear Recurrent Formula. The results indicate that the proposed method is more reliable in comparison to the widely used approaches that were proposed along with SSA. We conduct simulation studies to compare the relative performance of the new method with the existing ...

Extensions of singular spectrum analysis (SSA) for processing of non-rectangular images and time series with gaps are considered. A circular version is suggested, which allows application of the method to the data given on a circle or on a cylinder, e.g. cylindrical projection of a 3D ellipsoid. The constructed Shaped SSA method with planar or circular topology is able to produce low-rank appro...