A Note on Window Length Selection in Singular Spectrum Analysis

2013 ◽  
Vol 55 (2) ◽  
pp. 87-108 ◽  
Author(s):  
M. Atikur Rahman Khan ◽  
D. S. Poskitt
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Window Length ◽  
Singular Spectrum

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%.

Akurasi Peramalan Long Horizon dengan Singular Spectrum Analysis

2019 ◽  
Vol 3 (2) ◽  
pp. 93-99
Author(s):  
Awit Marwati Sakinah
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Window Length ◽  
Singular Spectrum

Perubahan iklim akhir-akhir ini tidak dapat dihindari. Salah satu penyebab perubahan iklim adalah perubahan suhu udara. Untuk itu, perlu dilakukan peramalan suhu agar penyimpangannya dapat diantisipasi. Dalam penelitian ini akan dibandingkan akurasi hasil peramalan dengan menggunakan model Singular Spectrum Analysis (SSA) dengan R-forecasting dan V-Forecasting. Peramalan dengan metode SSA R-forecasting dan V-Forecasting pada suhu Jakarta menggunakan window length L= 204 dan r=3 menghasilkan ramalan yang tidak jauh berbeda (aproksimasi kekontinuannya hampir sama). Berdasarkan hasil analisis, didapat MAPE untuk hasil permalan dengan SSA R-forecasting sebesar 5,0029 yang lebih besar dari MAPE SSA V-Forecasting sebesar 4,0067. Ini munjukkan bahwa peramalan suhu untuk long horizon lebih akurat dengan menggunakan V-Forecasting dibandingkan dengan R-Forecasting.

Separability and window length in singular spectrum analysis

2011 ◽  
Vol 349 (17-18) ◽  
pp. 987-990 ◽  
Author(s):  
Hossein Hassani ◽  
Rahim Mahmoudvand ◽  
Mohammad Zokaei
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Window Length ◽  
Singular Spectrum

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 Predicting COVID-19 Pandemic in Saudi Arabia Using Modified Singular Spectrum Analysis (Preprint)

2020 ◽  
Author(s):  
Nader Alharbi
Keyword(s):  
Saudi Arabia ◽  
Spectrum Analysis ◽  
Chaotic Systems ◽  
Singular Spectrum Analysis ◽  
Future Research ◽  
Good Prediction ◽  
Window Length ◽  
Second Stage ◽  
Singular Spectrum ◽  
Data Points

BACKGROUND This research presents a modified Singular Spectrum Analysis (SSA) approach for the analysis of COVID-19 in Saudi Arabia. We have proposed this approach and developed it in [1,2,3] for separability and grouping step in SSA, which plays an important role for reconstruction and forecasting in the SSA. The modified SSA mainly enables us to identify the number of the interpretable components required for separability, signal extraction and noise reduction. The approach was examined using different number of simulated and real data with different structures and signal to noise ratio. In this study we examine its capability in analysing COVID-19 data. Then, we use Vector SSA for predicting new data points and the peak of this pandemic. The results shows that the approach can be used as a promising one in decomposing and forecasting the daily cases of COVID-19 in Saudi Arabia. OBJECTIVE In this study we examine its capability in analysing COVID-19 data. Then, we use Vector SSA for predicting new data points and the peak of this pandemic METHODS Modified Singular Spectrum Analysis RESULTS A modified Singular Spectrum Analysis approach were used in this research for the decomposing and forecasting COVID-19 data in Saudi Arabia. The approach was examined in our previous research, and here in analysing COVID-19 data. In the first stage, the first 42 confirmed daily values (02-03 to 12-04-2020) were used and analysed to identify the value of r for separability between noise and the signal. After obtaining the value of r, which was 2, and extracting the signals, the Vector SSA were used for prediction and determine the pandemic peak. In the second stage,we updated the data and included 71 daily values. We have used the same window length and number of eigenvalues for reconstruction and forecasting. The results of both forecasting scenarios have indicated that the peak will be around end of May and mid of June, and the end of this crises will be between end of June and mid of July. All our results confirm the impressive performance of the modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from anoisy time series, and then make a good prediction using Vector SSA method. Note that we have not examined all possible values of window length in this research, and for forecasting we have used only the basic Vector SSA. For future research, we will include more data and considered different window length that may give a better forecasting. In addition, chaotic behaviour in COVID-19 data will be examined as we have some results that show strange patterns, which can be found in chaotic systems CONCLUSIONS A modified Singular Spectrum Analysis approach were used in this research for the decomposing and forecasting COVID-19 data in Saudi Arabia. The approach was examined in our previous research, and here in analysing COVID-19 data. In the first stage, the first 42 confirmed daily values (02-03 to 12-04-2020) were used and analysed to identify the value of r for separability between noise and the signal. After obtaining the value of r, which was 2, and extracting the signals, the Vector SSA were used for prediction and determine the pandemic peak. In the second stage,we updated the data and included 71 daily values. We have used the same window length and number of eigenvalues for reconstruction and forecasting. The results of both forecasting scenarios have indicated that the peak will be around end of May and mid of June, and the end of this crises will be between end of June and mid of July. All our results confirm the impressive performance of the modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from anoisy time series, and then make a good prediction using Vector SSA method. Note that we have not examined all possible values of window length in this research, and for forecasting we have used only the basic Vector SSA. For future research, we will include more data and considered different window length that may give a better forecasting. In addition, chaotic behaviour in COVID-19 data will be examined as we have some results that show strange patterns, which can be found in chaotic systems

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%.

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