scholarly journals PREDIKSI OUTFLOW UANG KARTAL DI KALIMANTAN BARAT DENGAN METODE SINGULAR SPECTRUM ANALYSIS (SSA)

2019 ◽  
Vol 8 (3) ◽  
Author(s):  
Ageng Wicaksono, Helmi, Yundari
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Singular Spectrum ◽  
Tracking Signal

Uang merupakan komponen penting dalam perekonomian yang digunakan sebagai alat pembayaran dalam melakukan transaksi jual dan beli. Banyaknya uang yang beredar di masyarakat berpengaruh pada kondisi perekonomian suatu negara. Dalam kewenangan untuk mengatur transaksi arus keluar/masuk uang kartal, diperlukan Rencana Kebutuhan Uang (RKU). Banyak aspek yang menjadi dasar pembuatan RKU, salah satunya data historis aliran uang masuk (inflow) dan aliran uang keluar (outflow). Agar perencanaan tersebut tepat maka peramalan outflow sangat diperlukan. Data outflow pada umumnya memuat pola musiman dan trend sehingga dapat dimodelkan dengan analisis deret waktu. Metode SSA merupakan metode yang menggunakan pendekatan nonparametrik. Artinya dalam pengaplikasiannya, metode ini tidak membutuhkan uji asumsi-asumsi parametrik. Metode SSA menguraikan data deret waktu ke dalam komponen-komponen, yaitu trend, musiman, siklis dan noise. Pada penelitian ini metode SSA yang digunakan adalah metode recurrent (R-forecasting). Penelitian ini dilakukan dengan mengelompokkan data sebanyak 3 kelompok yang menggambarkan fluktuasi data untuk 9 periode peramalan. Tingkat akurasi peramalan diukur menggunakan kriteria Mean Absolute Percentage Error (MAPE) dan Tracking Signal. MAPE dan Tracking Signal   pada   peramalan SSA terhadap data aktual sebesar 18,63% dan 0,6377. Hal ini mengakibatkan metode SSA dapat dikatakan baik dan valid untuk meramalkan outflow uang kartal di Kalimantan Barat periode November 2018 sampai Juli 2019.Kata Kunci: singular spectrum analysis, R-forecasting, MAPE, tracking signal

scholarly journals PERBANDINGAN KEAKURATAN HASIL PERAMALAN PRODUKSI BAWANG MERAH METODE HOLT-WINTERS DENGAN SINGULAR SPECTRUM ANALYSIS (SSA)

2017 ◽  
Vol 3 (1) ◽  
pp. 13
Author(s):  
Yogo Aryo Jatmiko ◽  
Rini Luciani Rahayu ◽  
Gumgum Darmawan
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Singular Spectrum ◽  
Tracking Signal

<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>

scholarly journals FORECASTING DATA RUNTUN WAKTU MUSIMAN MENGGUNAKAN METODE SINGULAR SPECTRUM ANALYSIS (SSA)

2020 ◽  
Vol 5 (1) ◽  
pp. 47
Author(s):  
Dadang Ruhiat ◽  
Dini Andiani ◽  
Wulan Nurul Kamilah
Keyword(s):  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Absolute Percentage Error ◽  
Singular Spectrum

Pemodelan dan forecasting data runtun waktu akhir-akhir ini terus berkembang dan digunakan di berbagai bidang termasuk di bidang hidrologi. Parameter hidrologi yang sangat penting adalah debit sungai di Indonesia sebagaimana halnya di negara tropis lainnya, besaran dan fluktuasinya dipengaruhi oleh dua faktor musiman, yaitu musim hujan dan kemarau. Pemodelan dan forecasting runtun waktu berbasis statistik pada dasarnya dapat dilakukan melalui dua pendekatan, yaitu statistik parametrik dan statistik non-parametrik. Namun fakta menunjukkan pemodelan dan forecasting runtun waktu melalui pendekatan statistik parametrik  lebih banyak dilakukan. Pada penelitian ini dilakukan pemodelan dan forecasting data runtun waktu debit sungai melalui pendekatan statistik non-parametrik dengan menggunakan metode Singular Spectrum Analysis (SSA).  Tujuan dari penelitian ini adalah untuk mengetahui hasil forecasting metode SSA dan mengetahui hasil komparasinya dengan hasil forecasting statistik parametrik yang telah dilakukan sebelumnya melalui model Seasonal Autoregressive Integrated Moving Average (SARIMA). Metode proses penelitian ini adalah berupa kajian teori yang kemudian dilanjutkan dengan proses komputasi. Hasil analisis menunjukkan metode SSA memberikan hasil forecasting dengan nilai Mean Absolute Percentage Error (MAPE) yang  lebih kecil dari model SARIMA. Dengan demikian disimpulkan forecasting runtun waktu debit sungai yang berpola musiman melalui metode SSA relatif lebih baik dari hasil forecasting model SARIMA.

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
Keyword(s):  
Gross Domestic Product ◽  
Spectrum Analysis ◽  
Time Series Data ◽  
Seasonal Pattern ◽  
Singular Spectrum Analysis ◽  
Domestic Product ◽  
Series Data ◽  
Percentage Error ◽  
Singular Spectrum ◽  
Tracking Signal

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)

scholarly journals Perbandingan Keakuratan Hasil Peramalan Produksi Bawang Merah Metode Holt-Winters dengan Singular Spectrum Analysis (SSA)

2017 ◽  
Vol 3 (1) ◽  
pp. 13-22
Author(s):  
Yogo Aryo Jatmiko ◽  
Rini Luciani Rahayu ◽  
Gumgum Darmawan
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Percentage Error ◽  
Seasonal Patterns ◽  
Model Data ◽  
Vector Method ◽  
Singular Spectrum ◽  
The Difference ◽  
Method R ◽  
Tracking Signal

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: 

scholarly journals Inflation Forecasting for East Kalimantan Province Using Hybrid Singular Spectrum Analysis- Autoregressive Integrated Moving Average Model

2021 ◽  
Vol 18 (1) ◽  
pp. 78-92
Author(s):  
Melisa Arumsari ◽  
Sri Wahyuningsih ◽  
Meiliyani Siringoringo
Keyword(s):  
Hybrid Model ◽  
Spectrum Analysis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Percentage Error ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Singular Spectrum ◽  
East Kalimantan ◽  
Inflation Forecasting

The Singular Spectrum Analysis (SSA)-Autoregressive Integrated Moving Average (ARIMA) hybrid method is a good combination of forecasting methods to improve forecasting accuracy and is suitable for economic data that tends to have trend and seasonal patterns, one of which is inflation data. The purpose of this study is to obtain the results of inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA hybrid model. The results of the inflation forecasting for East Kalimantan Province in 2021 using the SSA-ARIMA(1,1,1) hybrid model overall experienced an increase and the highest inflation in 2021 occurred in December of 0.92% with a forecasting accuracy level based on the Root Mean Square Error (RMSE) was 0.069399 and Mean Absolute Percentage Error (MAPE) was 32.61084%  

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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