Vector Singular Spectrum Analysis Research Articles

Prediction of Rainfall Trends Using Forecasting Approaches Based on Singular Spectrum Analysis

Advanced technologies such as the Internet of Things provide an integrated platform for weather focusing, including rainfall and flood prediction. Large rainfall data frequently contain noise, which can be difficult to analyze using a standard time series model due to violated assumptions. Singular spectrum analysis (SSA) is a model-free time series analysis method that is widely used. This study aims to predict the rainfall trends in the Special Region of Yogyakarta, Indonesia, using the Recurrent SSA (SSA-R) and Vector SSA (SSA-V). The SSA-R forecasts using the recurrent continuation directly with the linear recurrent formula, while the SSA-V is a modified recurrent method. This study used 50 years of monthly rainfall data (1970-2019) from 25 stations in the special region of Yogyakarta, Indonesia. The SSA steps for forecasting rainfall data include decomposition (embedding and singular value decomposition), reconstruction (grouping and diagonal averaging), and evaluating the SSA model using w-correlation (if w-correlation is close to zero, returning to the decomposition stage; otherwise, continue the process), forecasting, evaluating the forecast results using root mean square error (RMSE), mean absolute error, r, and mean forecast error, and finally selecting the best model (either the SSA-R or SSA-V model). The results showed that the SSA-R performed better than SSA-V due to the smallest RMSE in the dry, rainy, and inter-monsoon seasons. The SSA-R model’s forecast results revealed faint, constant patterns for the dry, and rainy seasons and an increasing pattern for the inter-monsoon season. The novelty of this study is to compare the performance of the SSA-R and SSA-V models in the large rainfall data in the special region of Yogyakarta, Indonesia.

Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis

BackgroundInfectious disease is one of the main issues that threatens human health worldwide. The 2019 outbreak of the new coronavirus SARS-CoV-2, which causes the disease COVID-19, has become a serious global pandemic. Many attempts have been made to forecast the spread of the disease using various methods, including time series models. Among the attempts to model the pandemic, to the best of our knowledge, no studies have used the singular spectrum analysis (SSA) technique to forecast confirmed cases.ObjectiveThe primary objective of this paper is to construct a reliable, robust, and interpretable model for describing, decomposing, and forecasting the number of confirmed cases of COVID-19 and predicting the peak of the pandemic in Saudi Arabia.MethodsA modified singular spectrum analysis (SSA) approach was applied for the analysis of the COVID-19 pandemic in Saudi Arabia. We proposed this approach and developed it in our previous studies regarding the separability and grouping steps in SSA, which play important roles in reconstruction and forecasting. The modified SSA approach mainly enables us to identify the number of interpretable components required for separability, signal extraction, and noise reduction. The approach was examined using different levels of simulated and real data with different structures and signal-to-noise ratios. In this study, we examined the capability of the approach to analyze COVID-19 data. We then used vector SSA to predict new data points and the peak of the pandemic in Saudi Arabia.ResultsIn the first stage, the confirmed daily cases on the first 42 days (March 02 to April 12, 2020) were used and analyzed to identify the value of the number of required eigenvalues (r) for separability between noise and signal. After obtaining the value of r, which was 2, and extracting the signals, vector SSA was used to predict and determine the pandemic peak. In the second stage, we updated the data and included 81 daily case values. We used the same window length and number of eigenvalues for reconstruction and forecasting of the points 90 days ahead. The results of both forecasting scenarios indicated that the peak would occur around the end of May or June 2020 and that the crisis would end between the end of June and the middle of August 2020, with a total number of infected people of approximately 330,000.ConclusionsOur results confirm the impressive performance of modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from a noisy time series and then making a reliable prediction of daily confirmed cases using the vector SSA method.

A novel approach for predicting burned forest area

Forest fire hazard is a major problem in the Mediterranean region of Turkey and has a significant effect on both the climate system and ecosystems. During the last century, many forest fires accounted for the majority of the Mediterranean region in Turkey. Vector singular spectrum analysis (V-SSA) and vector multivariate singular spectrum analysis (V-MSSA) are relatively novel but powerful time series analysis techniques. The present study addresses how to forecast burned forest area (BFA) by V-SSA. One of the most important factors affecting forest fires is weather conditions. The prediction of BFA is therefore also obtained by V-MSSA using meteorological covariates (i.e., relative humidity (RH), temperature (T) and wind speed (WS). In the study, forest fire data records covering the years 2005–2019 were collected and analyzed. To gain forecast accuracy, the years 2017–2019 were used as testing data, and forecast values for 1, 3, 6, 12, 24 and 36 months were obtained. Then, V-SSA and V-MSSA models were compared via the root mean square errors (RMSEs) to reach the best model explaining BFA. Our results indicated that the RMSEs of the eight models were low and close to each other. Further, forecasts for the months of the years 2020–2022 were obtained and compared with actual BFA values by means of the RMSEs. According to RMSEs, the best forecasts are obtained using the V-MSSA model with meteorological covariates BFA, WS and T.

PERAMALAN NILAI TUKAR PETANI MENGGUNAKAN METODE SINGULAR SPECTRUM ANALYSIS

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.

Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis

Accurate forecasts of home sales can provide valuable information for not only, policy makers, but also financial institutions and real estate professionals. Given this, our analysis compares the ability of two different versions of singular spectrum analysis (SSA) methods, namely recurrent SSA (RSSA) and vector SSA (VSSA), in univariate (UV) and multivariate (MV) frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy and its four census regions (Northeast, Midwest, South and West). We compare the performance of the SSA-based models with classical and Bayesian variants of the autoregressive (AR) and vector AR models. Using an out-of-sample period of 1979:8---2014:6, given an in-sample period of 1973:1---1979:7, we find that the UVVSSA is the best performing model for the aggregate US home sales, while the MV versions of the RSSA is the outright favorite in forecasting home sales for all the four census regions. Our results highlight the superiority of the nonparametric approach of the SSA, which in turn, allows us to handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or non-Gaussian.

A Preliminary Investigation into the Effect of Outlier(s) on Singular Spectrum Analysis

The aim of this paper is to study the effect of outliers on different parts of singular spectrum analysis (SSA) from both theoretical and practical points of view. The rank of the trajectory matrix, the magnitude of eigenvalues, reconstruction, and forecasting results are evaluated using simulated and real data sets. The performance of both recurrent and vector forecasting procedures are assessed in the presence of outliers. We find that the existence of outliers affect the rank of the matrix and increases the linear recurrent dimensions whilst also having a significant impact on SSA reconstruction and forecasting processes. There is also evidence to suggest that in the presence of outliers, the vector SSA forecasts are more robust in comparison to the recurrent SSA forecasts. These results indicate that the identification and removal of the outliers are mandatory to achieve optimal SSA decomposition and forecasting results.

Forecasting exchange rates: An optimal approach

This paper looks at forecasting daily exchange rates for the United Kingdom, European Union, and China. Here, the authors evaluate the forecasting performance of neural networks (NN), vector singular spectrum analysis (VSSA), and recurrent singular spectrum analysis (RSSA) for forecasting exchange rates in these countries. The authors find statistically significant evidence based on the RMSE, that both VSSA and RSSA models outperform NN at forecasting the highly unpredictable exchange rates for China. However, the authors find no evidence to suggest any difference between the forecasting accuracy of the three models for UK and EU exchange rates.