scholarly journals Window Length Selection of Singular Spectrum Analysis and Application to Precipitation Time Series

2017 ◽  
Vol 19 (2) ◽  
pp. 306-317 ◽  
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Precipitation Data ◽  
Window Length ◽  
Analysis Method ◽  
Precipitation Time Series ◽  
Singular Spectrum ◽  
Periodogram Analysis ◽  
Selection Of

Window length is a very critical tuning parameter in Singular Spectrum Analysis (SSA) technique. For finding the optimal value of window length in SSA application, Periodogram analysis method with SSA for referencing on the selection of window length and confirm that the periodogram analysis can provide a good option for window length selection in the application of SSA. Several potential periods of Florida precipitation data are firstly obtained using periodogram analysis method. The SSA technique is applied to precipitation data with different window length as the period and experiential recommendation to extract the precipitation time series, which determines the leading components for reconstructing the precipitation and forecast respectively. A regressive model linear recurrent formula (LRF) model is used to discover physically evolution with the SSA modes of precipitation variability. Precipitation forecasts are deduced from SSA patterns and compared with observed precipitation. Comparison of forecasting results with observed precipitation indicates that the forecasts with window length of L=60 have the better performance among all. Our findings successfully confirm that the periodogram analysis can provide a good option for window length selection in the application of SSA and presents a detailed physical explanation on the varying conditions of precipitation variables.

scholarly journals PERAMALAN BEBAN LISTRIK DAERAH ISTIMEWA YOGYAKARTA DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA)

2019 ◽  
Vol 12 (2) ◽  
pp. 214
Author(s):  
Herni Utami ◽  
Yunita Wulan Sari ◽  
Subanar Subanar ◽  
Abdurakhman Abdurakhman ◽  
Gunardi Gunardi
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Electricity Demand ◽  
Signal Extraction ◽  
Forecasting Model ◽  
Window Length ◽  
Singular Spectral Analysis ◽  
Singular Spectrum

This paper will study forecasting model for electricity demand in Yogyakarta and forecast it for 2019 until 2024. Usually, electricity demand data contain seasonal. We propose Singular Spectral Analysis-Linear Recurrent Formula (SSA-LRF) method. The SSA process consists of decomposing a time series for signal extraction and then reconstructing a less noisy series which is used for forecasting. The SSA-LRF method will be used to forecast h-step ahead. In this study, we use monthly electricity demand in Yogyakarta for 11 year (2008 to 2018). The forecasting results indicates that the forecast using window length of L=26 have good performance with MAPE of 1.9%.

Selection of window length for singular spectrum analysis

2015 ◽  
Vol 352 (4) ◽  
pp. 1541-1560 ◽  
Author(s):  
Rui Wang ◽  
Hong-Guang Ma ◽  
Guo-Qing Liu ◽  
Dong-Guang Zuo
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Window Length ◽  
Singular Spectrum ◽  
Selection Of

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA KE BALI MENGGUNAKAN METODE SINGULAR SPECTRUM ANALYSIS

2019 ◽  
Vol 8 (4) ◽  
pp. 303
Author(s):  
MIRA AYU NOVITA SARI ◽  
I WAYAN SUMARJAYA ◽  
MADE SUSILAWATI
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Window Length ◽  
Original Time Series ◽  
Number Of Components ◽  
Singular Spectrum ◽  
Original Time

Singular spectrum analysis (SSA) is a method to decompose the original time series into a summation of a small number of components that can be interpreted as varied trends, oscillatory, and noise components. The purpose of this research is to model and to find out the results of forecasting the number of foreign tourists arrival to Bali using SSA method. In this research, the accuracy of forecasting results will be calculated using the SSA model with reccurent singular spectrum analysis (RSSA) method. The best SSA model was obtained with a window length (L=94) and produces MAPE value of 7,65%.

scholarly journals PERAMALAN NILAI TUKAR PETANI MENGGUNAKAN METODE SINGULAR SPECTRUM ANALYSIS

2020 ◽  
Vol 9 (3) ◽  
pp. 171
Author(s):  
GILANG BIMASAKTI ANDHIKA ◽  
I WAYAN SUMARJAYA ◽  
I GUSTI AYU MADE SRINADI
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Analysis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Window Length ◽  
Nonparametric Approach ◽  
Singular Spectrum ◽  
Forecasting Method ◽  
L Value

Singular spectrum analysis (SSA) is a new method in time series analysis that uses a nonparametric approach. The purpose of this study is to determine the model and forecast the farmer exchange rate in the Province of Bali using SSA. Vector singular spectrum analysis (VSSA) forecasting method is used to calculate the accuracy of forecasting. The best SSA model is obtained with a window length (L) value of 57 and produces a MAPE value of 0.49%. In conclusion, SSA method can predict farmer exchange rate in the Province of Bali very accurate.

Application of singular spectrum analysis in reconstruction of the annual signal from GRACE

2020 ◽  
Vol 14 (3) ◽  
pp. 295-302
Author(s):  
Chuandong Zhu ◽  
Wei Zhan ◽  
Jinzhao Liu ◽  
Ming Chen
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mixture Effect ◽  
Singular Spectrum ◽  
Grace Data ◽  
Terrestrial Water ◽  
Annual Signal ◽  
Long Term Variations

AbstractThe mixture effect of the long-term variations is a main challenge in single channel singular spectrum analysis (SSA) for the reconstruction of the annual signal from GRACE data. In this paper, a nonlinear long-term variations deduction method is used to improve the accuracy of annual signal reconstructed from GRACE data using SSA. Our method can identify and eliminate the nonlinear long-term variations of the equivalent water height time series recovered from GRACE. Therefore the mixture effect of the long-term variations can be avoided in the annual modes of SSA. For the global terrestrial water recovered from GRACE, the peak to peak value of the annual signal is between 1.4 cm and 126.9 cm, with an average of 11.7 cm. After the long-term and the annual term have been deducted, the standard deviation of residual time series is between 0.9 cm and 9.9 cm, with an average of 2.1 cm. Compared with the traditional least squares fitting method, our method can reflect the dynamic change of the annual signal in global terrestrial water, more accurately with an uncertainty of between 0.3 cm and 2.9 cm.

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