The purpose of this paper is to study hourly time series temperature and humidity from six meteorological stations in Oman 2009 2018. Since our data contains missing values due device failures, we have compared several methods imputation found that regression with lagging most reasonable. To extract the annual oscillations daily periodicities apply Singular Spectrum Analysis. By applying Mann–K...

In this paper, we discuss the efficiency of noise reduction for curve fitting in growth curve models. We use singular spectrum analysis as a nonlinear–nonparametric denoising method. A set of longitudinal measurements is used in considering the performance of the method. The results show that noise reduction is important for curve fitting in growth curve model and also, that the singular spectr...

Singular spectrum analysis of time series observations can be visualized as a sliding window of width m moving down the time series x of length n, to determine the orthogonal patterns that best capture the variance presented by the window. It is designed to extract information from short and noisy time series. This allows the time series to be decomposed into various components, ideally separat...

Change Point Discovery is a basic algorithm needed in many time series mining applications including rule discovery, motif discovery, casual analysis, etc. Several techniques for change point discovery have been suggested including wavelet analysis, cosine transforms, CUMSUM, and Singular Spectrum Transform. Of these methods Singular Spectrum Transform (SST) have received much attention because...