scholarly journals PERAMALAN PRODUK DOMESTIK BRUTO (PDB) SEKTOR PERTANIAN, KEHUTANAN, DAN ‎PERIKANAN MENGGUNAKAN SINGULAR SPECTRUM ANALYSIS (SSA)

2019 ◽  
Vol 8 (1) ◽  
pp. 68-80
Author(s):  
Desy Tresnowati Hardi ◽  
Diah Safitri ◽  
Agus Rusgiyono

Forecasting is the process of estimating conditions in the future by testing conditions from the past. One of the forecasting methods is Singular Spectrum Analysis (SSA) which aim of SSA is to make a decomposition of the original series into the sum of a small number of independent and interpretable components such as a slowly varying trend, oscillatory components and a structureless noise. Gross Domestic Product data in the agriculture, forestry, and fisheries sector are time series data with trend and seasonal pattern so that it can be processed using the SSA method. The forecasting process of SSA method uses the main parameter (L) of 21 obtained by the Blind Source Separation (BSS) method. From forecasting, acquired group of 3 groups. Forecasting resulted the value of Mean Absolute Percentage Error (MAPE) is 1.59% and the value of tracking signal is 2.50, which indicates that the results of forecasting is accurate. Keywords: Forecasting, Gross Domestic Product in the agriculture, forestry, and fisheries sector, Singular Spectrum Analysis (SSA)

2018 ◽  
Vol 17 (02) ◽  
pp. 1850017 ◽  
Author(s):  
Mahdi Kalantari ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani ◽  
Emmanuel Sirimal Silva

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the [Formula: see text] norm-based version of Singular Spectrum Analysis (SSA), namely [Formula: see text]-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially [Formula: see text]-SSA can provide better imputation in comparison to other methods.


2015 ◽  
Vol 79 (1) ◽  
pp. 317-330 ◽  
Author(s):  
Arvind Kumar ◽  
Vivek Walia ◽  
Baldev Raj Arora ◽  
Tsanyao Frank Yang ◽  
Shih-Jung Lin ◽  
...  

Author(s):  
S.M. Shaharudin ◽  
N. Ahmad ◽  
N.H. Zainuddin

<p>Identifying the local time scale of the torrential rainfall pattern through Singular Spectrum Analysis (SSA) is useful to separate the trend and noise components. However, SSA poses two main issues which are torrential rainfall time series data have coinciding singular values and the leading components from eigenvector obtained from the decomposing time series matrix are usually assesed by graphical inference lacking in a specific statistical measure. In consequences to both issues, the extracted trend from SSA tended to flatten out and did not show any distinct pattern.  This problem was approached in two ways. First, an Iterative Oblique SSA (Iterative O-SSA) was presented to make adjustment to the singular values data. Second, a measure was introduced to group the decomposed eigenvector based on Robust Sparse K-means (RSK-Means). As the results, the extracted trend using modification of SSA appeared to fit the original time series and looked more flexible compared to SSA.</p>


2017 ◽  
Vol 3 (1) ◽  
pp. 13-22
Author(s):  
Yogo Aryo Jatmiko ◽  
Rini Luciani Rahayu ◽  
Gumgum Darmawan

The Holt-Winters method is used to model data with seasonal patterns, whether trends or not. There are two methods of forecasting in Singular Spectrum Analysis (SSA), namely recurrent method (R-forecasting) and vector method (V-forecasting). The recurrent method performs continuous continuation (with the help of LRF), whereas the vector method corresponds to the L-continuation. Different methods of course make a difference in the accuracy of forecast results. To see the difference between the three methods is done by looking at the comparison of accuracy and reliability of forecast results. To measure the accuracy of forecasting used Mean Absolute Percentage Error (MAPE) and to measure the reliability of forecasting results is done by tracking signal. Applications are done on Indonesian red onion production from January 2006 to December 2015. Forecasting of both methods in SSA uses window length L = 39 and grouping r = 8. With α = 0.1, β = 0.001 and γ = 0.5, Holt-Winters additive method gives better result with MAPE 13,469% than SSA method.   Keywords: 


2014 ◽  
Vol 1 (2) ◽  
pp. 1947-1966
Author(s):  
Y. Shen ◽  
F. Peng ◽  
B. Li

Abstract. Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution will develop an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach was evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60%, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30% for the first four principal components, respectively. Besides, both the mean absolute errors and mean root mean squared errors of the reconstructed time series by ISSA are also much smaller than those by SSAM. The respective improvements are 34.45 and 33.91% when the missing data accounts for 60%. The results from real incomplete time series also show that the SD derived by ISSA is 12.27 mg L−1, smaller than 13.48 mg L−1 derived by SSAM.


2021 ◽  
Author(s):  
Shu Kaneko ◽  
Katsumi Hattori ◽  
Toru Mogi ◽  
Chie Yoshino

&lt;p&gt;Off the coast of the Boso Peninsula, there is a triple junction of the Pacific Plate, the Philippine Sea Plate, and the North American Plate and the Boso Peninsula is one of the seismically active areas in Japan. There are also epicenter areas such as the 1703 Genroku Kanto Earthquake (M8.2), the 1923 Taisho Kanto Earthquake (M7.9), and the Boso Slow Slip which occurs every 6 years, which are geologically interesting places. To estimate the subsurface resistivity structure of the whole Boso area, Magnetotelluric (MT) survey with 41 sites (inter-sites distance of 7 km) has been conducted in 2014-2016, using U43 (12 sites, 1 Hz sampling ; Tierra Technica) and MTU-5, 5A, net (41 sites, 15, 150, and 2400 Hz sampling; Phoenix Geophysics). However, the Boso area is greatly affected by leak current from DC-driven trains, factories, and power lines, so the observed data are contaminated by artificial noises. When we tried to apply the conventional noise reduction method (e.g., remote reference (Gamble et al., 1979) and BIRRP (Chave and Thomson, 2004)) in frequency domain, the obtained MT sounding curve was not ideal. In particular, the phase between the periods of 20 and 400 sec was close to 0 degrees. It suggests that the method used is insufficient to reduce the near-field effect for the Boso data. Thus, we developed a new noise reduction method using MSSA (Multi-channel Singular Spectrum Analysis) as a pre-processing method in time domain.&lt;/p&gt;&lt;p&gt;The procedure is as follows;&lt;/p&gt;&lt;p&gt;(1) Decompose 6 component data (Hx, Hy, Ex, Ey, Hxr and Hyr: H and E means magnetic and electric field, respectively, x and y indicates NS and EW component, and r denotes the reference field observed at a quiet station) using MSSA into 6&amp;#215;M principal components (PCs). &amp;#160;Here, M shows the window length of MSSA.&lt;/p&gt;&lt;p&gt;(2) Check contribution and periods of each PC and eliminate the PCs which are corresponding to the longer periods of variation. That is &amp;#8220;detrend&amp;#8221; of the original data.&lt;/p&gt;&lt;p&gt;(3) Apply the second MSSA to the detrended time series data to separate signals and noises shorter than 400 sec.&lt;/p&gt;&lt;p&gt;(4) Calculating correlation coefficients between H and Hr and between E and Hr for each PC and select the PCs with higher correlation to reconstruct time series data to make MT analysis.&lt;/p&gt;&lt;p&gt;Then, we perform MT analysis by BIRRP to estimate apparent resistivity,&lt;/p&gt;&lt;p&gt;As a result, the coherences of H-Hr, and E-Hr were improved and the MT sounding curve became smoother than those results by the conventional noise reduction methods. This indicated that the effectiveness of the proposed noise reduction. However, further investigation in different periods and sites will be required.&lt;/p&gt;


2017 ◽  
Vol 3 (1) ◽  
pp. 13
Author(s):  
Yogo Aryo Jatmiko ◽  
Rini Luciani Rahayu ◽  
Gumgum Darmawan

<p>Metode <em>Holt-Winters </em>digunakan untuk memodelkan data dengan pola musiman, baik mengandung trend maupun tidak<em>. </em>Terdapat dua metode peramalan dalam <em>Singular Spectrum Analysis</em> (SSA), yaitu metode rekuren (<em>R-forecasting</em>) dan metode vektor (<em>V-forecasting</em>). Metode rekuren melakukan kontinuasi secara langsung (dengan bantuan LRF), sedangkan metode vektor berhubungan dengan <em>L-continuation</em>. Perbedaan metode tentunya memberikan perbedaan dalam keakuratan hasil ramalan. Untuk melihat perbedaan antara ketiga metode tersebut dilakukan dengan melihat perbandingan keakuratan dan keandalan hasil ramalan. Untuk mengukur ketepatan peramalan digunakan <em>Mean Absolute Percentage Error</em> (MAPE) dan untuk mengukur keandalan hasil peramalan dilakukan dengan <em>tracking signal</em>. Aplikasi dilakukan pada produksi bawang merah Indonesia periode Januari 2006-Desember 2015. Peramalan kedua metode di SSA menggunakan <em>window</em> <em>length</em> L=39 dan <em>grouping</em> r=8. Dengan nilai α = 0.1,  β= 0.001 dan γ=0.5, metode <em>Holt-</em><em>Winters</em> <em>additive</em> memberikan hasil yang lebih baik dengan MAPE 13,469% dibanding metode <em>SSA.</em></p>


2015 ◽  
Vol 22 (4) ◽  
pp. 371-376 ◽  
Author(s):  
Y. Shen ◽  
F. Peng ◽  
B. Li

Abstract. Singular spectrum analysis (SSA) is a powerful technique for time series analysis. Based on the property that the original time series can be reproduced from its principal components, this contribution develops an improved SSA (ISSA) for processing the incomplete time series and the modified SSA (SSAM) of Schoellhamer (2001) is its special case. The approach is evaluated with the synthetic and real incomplete time series data of suspended-sediment concentration from San Francisco Bay. The result from the synthetic time series with missing data shows that the relative errors of the principal components reconstructed by ISSA are much smaller than those reconstructed by SSAM. Moreover, when the percentage of the missing data over the whole time series reaches 60 %, the improvements of relative errors are up to 19.64, 41.34, 23.27 and 50.30 % for the first four principal components, respectively. Both the mean absolute error and mean root mean squared error of the reconstructed time series by ISSA are also smaller than those by SSAM. The respective improvements are 34.45 and 33.91 % when the missing data accounts for 60 %. The results from real incomplete time series also show that the standard deviation (SD) derived by ISSA is 12.27 mg L−1, smaller than the 13.48 mg L−1 derived by SSAM.


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