scholarly journals Forecasting of internet usage by singular spectrum analysis with trend extraction method

2019 ◽  
Author(s):  
Gumgum Darmawan ◽  
Dedi Rosadi ◽  
Budi Nurani Ruchjana ◽  
Hermansah
Keyword(s):  
Spectrum Analysis ◽  
Extraction Method ◽  
Singular Spectrum Analysis ◽  
Internet Usage ◽  
Singular Spectrum ◽  
Trend Extraction

scholarly journals Singular Spectrum Analysis of Gene Expression Profiles of EarlyDrosophila embryo: Exponential-in-Distance Patterns

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
T. Alexandrov ◽  
N. Golyandina ◽  
A. Spirov
Keyword(s):  
Gene Expression ◽  
Spectrum Analysis ◽  
Expression Profiles ◽  
Singular Spectrum Analysis ◽  
Gene Expression Profiles ◽  
Analytical Approximation ◽  
Similar Data ◽  
Singular Spectrum ◽  
Trend Extraction ◽  
The Given

We present investigation of gene expression profiles by means of singular spectrum analysis (SSA). The biological problem under investigation is the decomposition ofbicoidprotein profiles ofDrosophila melanogasterinto 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 processing problems addressed are (i) trend extraction from a noisy signal, (ii) batch processing of similar data, and (iii) analytical approximation of the signal components by the sum of exponential and constant-like functions. The proposed methods are evaluated on the given 17 series.

scholarly journals Electricity consumption forecasting using singular spectrum analysis

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 138-146 ◽  
Author(s):  
Moises Lima de Menezes ◽  
Reinaldo Castro Souza ◽  
José Francisco Moreira Pessanha
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Computational Experiment ◽  
Singular Spectrum Analysis ◽  
Electricity Consumption ◽  
Principal Component ◽  
Graphical Analysis ◽  
Singular Spectrum ◽  
Trend Extraction ◽  
Residential Consumption

Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

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