Singular Spectrum Analysis Technique Research Articles

A Method to Suppress Interferences Based on Secondary Compensation with QPC-FDA-MIMO Radar

The traditional phased array radar is unable to distinguish mainlobe deceptive interference from the true target when they share an identical angle. To address this issue, a method is developed to suppress the interference in the joint transmit–receive–Doppler frequency domain, using secondary compensation and quadratic phase code (QPC) with the frequency diversity array multiple-input multiple-output (FDA-MIMO) radar. The QPC technique is employed by encoding the transmitted pulses in various spatial channels, which extends the degrees-of-freedom (DOFs) of the system. To distinguish and suppress the false targets behind the true target by at least one pulse, Doppler compensation is performed and Doppler filters are employed. To distinguish the fast-generated false targets, which are located in the same pulse with the true target, the spatial frequency compensation is implemented, which equivalently relocates the mainlobe interference to the sidelobes. Via the singular spectrum analysis (SSA) technique and the data-dependent beamforming, the interferences within the ambiguity region are suppressed. Simulation results verify the effectiveness of the method.

Attentional ensemble model for accurate discharge and water level prediction with training data enhancement

Discharge is water flow from a higher to a lower elevation resulting from precipitation or surface runoff. It is an essential resource for human society, including drinking water and irrigation. Forecasting discharge and water level are crucial in establishing plans to secure water sources and predict floods. Deep learning has recently emerged as a potential solution for forecasting discharge and water levels. However, it faces three challenges: training data shortage, noise existence, and underestimation tendency. This research offers a novel deep learning-based method addressing the abovementioned shortcomings. To overcome data scarcity and improve prediction accuracy, we leverage the strengths of the one-dimensional Convolutional Neural Network (1D-CNN), Long short-term memory (LSTM), and Ensemble learning technique. Specifically, 1D-CNN uses a kernel moving in one direction and executing the convolution operations to extract the correlations between the features. LSTM helps to capture temporal relationships inside the data, while ensemble learning exploits multiple learning models for better predictive performance. Besides, Singular-Spectrum Analysis (SSA) technique is applied to decompose the input data into several components and eliminate noise-like ones while retaining essential parts. Besides, the attention mechanism is leveraged to assign higher weights for more essential features, further enhancing the accuracy. Finally, we utilize the Linear Exponential (LINEX) loss to prevent under- and overestimation. The experiments show that our proposal outperforms existing approaches regarding all metrics. Notably, our method improves the Nash–Sutcliffe model Efficiency coefficient (NSE) (the most critical metric) by up to 40% and 34% concerning one-step-ahead and multistep-ahead, respectively, compared to existing approaches.

Hourly significant wave height prediction via singular spectrum analysis and wavelet transform based models

Generation of significant wave height (SWH) is considered as a complex and stochastic dynamical process whose prediction is vital for effective marine, ocean engineering, sustainable development and scientific phenomena. Based on literature review, despite numerous research attempts to forecast significant wave height with short prediction time horizons, they are incapable in yielding accurate SWH predictions. To achieve this end wavelet techniques have been intensively employed as data pre-processing tools and, have been incorporated with soft computing based approaches to improve the prediction performance of developed models. However, wavelet algorithm has some limitations such as shift sensitivity, poor directionality and lack of phase information. In addition, this technique suffers from time consuming complicated mathematical procedures. In the present study, as a way of addressing the shortcomings of wavelet tool and enhancing prediction accuracy with extended time horizons, singular spectrum analysis (SSA) is proposed as a decomposition procedure. The prediction accuracy of the three distinct models is contrasted by means of diagnostic metrics, Mean Square Error (MSE), the Nash-Sutcliffe Coefficient of efficiency (CE) and determination of coefficient (R2). With its lowest MSE and highest CE value SSA-Fuzzy model clearly outperformed the stand alone Fuzzy and W-Fuzzy models in predicting hourly SWH for all stations and future time horizons. This implies that SSA technique has the utmost capability to decompose measured data effectively into its deterministic and stochastic components.

Singular spectrum analysis of time series data from low-frequency radiometers, with an application to SITARA data

ABSTRACTUnderstanding the temporal characteristics of data from low-frequency radio telescopes is of importance in devising suitable calibration strategies. Application of time-series analysis techniques to data from radio telescopes can reveal a wealth of information that can aid in calibration. In this paper, we investigate singular spectrum analysis (SSA) as an analysis tool for radio data. We show the intimate connection between SSA and Fourier techniques. We develop the relevant mathematics starting with an idealized periodic dataset and proceeding to include various non-ideal behaviours. We propose a novel technique to obtain long-term gain changes in data, leveraging the periodicity arising from sky drift through the antenna beams. We also simulate several plausible scenarios and apply the techniques to a 30-day time series data collected during 2021 June from SITARA – a short-spacing two element interferometer for global 21-cm detection. Applying the techniques to real data, we find that the first reconstructed component – the trend – has a strong anti-correlation with the local temperature suggesting temperature fluctuations as the most likely origin for the observed variations in the data. We also study the limitations of the calibration in the presence of diurnal gain variations and find that such variations are the likely impediment to calibrating SITARA data with SSA.

Application of the Kalman Filter/Smoother for accurate material characterization of planar dielectric samples by using free-space measurements at sub-THz frequencies

The Kalman Filter/Smoother, as a new post-calibration technique, has been successfully applied for accurate relative complex permittivity ɛr extraction of planar dielectric samples using sub-THz free-space transmission scattering parameter S21 measurements. Extracted ɛr of the planar polyvinylchloride and polymethylmethacrylate samples without any post-calibration procedure produced non-physical results (violation of the passivity principle) over some frequencies in three frequency bands between 50 and 75 GHz, 75 and 110 GHz, and 110 and 170 GHz, indicating the importance of the application of a post-calibration procedure. It has been demonstrated by S21 measurements and extracted ɛr that the Kalman Smoother and the Savitzky–Golay filter, as the other post-calibration technique, produce little offset or deviation in the extracted ɛr around the lowest and highest frequencies over the frequency band (or at the intersection of individual frequency bands), as compared with that of the singular spectrum analysis technique, considered as another post-calibration technique.

A two-step singular spectrum analysis method for robust low-rank approximation of seismic data

The singular spectrum analysis (SSA) method can detect the low-rank structure of data and therefore has become a powerful tool in seismic data processing and analysis. In particular, the SSA method can effectively suppress seismic random noise according to the different behaviors of coherent signal and random noise in the singular spectrum. However, there has been much research and experimentation indicating that the basic SSA method performs poorly when the noise becomes erratic. One of the reasons is that the quadratic misfit adopted in SSA is sensitive to non-Gaussian disturbances. One solution to this problem is to iteratively reweight the low-rank approximation of using the SSA method and finally achieve a particular kind of robust misfit. The low-rank approximation is robustified in this study by a more direct strategy, which consists of two main steps, prediction and elimination, and is called the two-step SSA method. The whole algorithm only runs the SSA filtering twice and hence is more computationally efficient compared to the iteratively reweighted SSA technique. The two-step SSA method adopts two criteria to calculate a weighting matrix and predict the erratic disturbance. This strategy also can be generalized into an iterative reweighting-based technique (e.g., the iteratively reweighted SSA technique) for robust denoising. The performance of the proposed two-step SSA method is tested using simulated and real seismic data. The results demonstrate its feasibility.

Free-breathing myocardial T1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction.

To develop a free-breathing myocardial mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction. Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incomplete recovery into account for an accurate calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction. In comparison to an IR spin-echo reference, phantom results confirm good accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improves precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardial maps can be obtained within 2 min with good precision and repeatability. Motion-resolved myocardial mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.

Improvement of an Industrial Robotic Flaw Detection System

Rotary encoders are commonly used for dynamic control and positioning of industrial robots. Results of this study suggested that rotary encoder signal can also be used to monitor the efficiency of industrial robot systems effectively after proper processing. A novel strategy using singular spectrum analysis (SSA) integrated with hierarchical hyper-Laplacian prior prototype (HHLP) is proposed in this study for defect detection in industrial robots. Sparsity-assisted techniques are efficient flaw-based extraction techniques that have been extensively investigated in recent years. However, the selection of an appropriate sparse prior from the point of view of probability theory remains unverified. First, SSA enables the separation of complicated encoding signals to several interpretable components, including a trend, a set of cyclic oscillations, and residual oscillations (noise). Second, we describe HHLP by maximizing posterior probability of robot flaw diagnosis. HHLP is proposed to extract noise interference, cyclic pulse, and harmonic interference from the residual signal using the SSA technique. We infer that the hyper-Laplacian prior in the prototype may present a more efficient prototype flaw than the Laplacian prior. In addition, HHLP incorporates physical characteristics necessary to distinguish harmonic intervention. This study primarily establishes a modern prototype that represents the former dispersed from the perspective of maximizing the probability of the latter. Meanwhile, generalized minimax-concave regularization inductive and kurtosis-based weighted sparse prototypes are compared and spectral kurtosis is used to confirm the efficacy of HHLP. Note to Practitioners—This study aims to solve the problem of industrial robot fault diagnosis during the operation process to avoid production delays. Rotary encoder sensor is attached to each joint to collect raw information and identify the robot position. Data from the rotary encoder sensor can also be used for the efficient health status assessment of the performance of the industrial robot system after proper processing. Therefore, a new approach using singular spectrum analysis combined with hierarchical hyper-Laplacian pre-induced prototype is proposed in this study. The residual signal extracted using the singular spectrum analysis method for processing in a hierarchical hyper-Laplacian pre-induced prototype is used to improve the weak fault feature. We describe a hierarchical hyper-Laplacian preprototype by maximizing the posterior probability of the flaw diagnosis. We introduce a hierarchical hyper-Laplacian prior that integrates physical characteristics to distinguish between harmonic interferences. The distribution of coefficients acquired through other dictionaries or transformations and selection of the optimal priority for the extraction of flaw characteristics will be the foci of future investigations.

SSA-CAE-Based Abnormal Data Classification Method in Edge Intelligence Device of CNC Machine

Smart factories and big data are important factors in the Fourth Industrial Revolution. Smart factories aim for automation and integration; however, the most important part is the application of data. Despite extensive research on the maintenance and quality management of big data-based production equipment, industrial data gathered for analysis contain more normal data than abnormal data. In addition, a significant amount of energy is expended in the data pre-processing process to analyze the acquired data. Therefore, to maintain production equipment and quality management, data classification technology that allows easy data analysis by classifying abnormal data into normal data is required. In this paper, we propose an abnormal data classification architecture for cycle data sets gathered from production facilities through SSA-CAE along with data storage methods for each product unit. SSA-CAE is a hybrid technique that combines singular spectrum analysis (SSA) techniques that are effective in reducing noise in time series data with convolutional auto encoder (CAE) that have performed well in time series.

Anomalous Vehicle Recognition in Smart Urban Traffic Monitoring as an Edge Service

The past decades witnessed an unprecedented urbanization and the proliferation of modern information and communication technologies (ICT), which makes the concept of Smart City feasible. Among various intelligent components, smart urban transportation monitoring is an essential part of smoothly operational smart cities. Although there is fast development of Smart Cities and the growth of Internet of Things (IoT), real-time anomalous behavior detection in Intelligent Transportation Systems (ITS) is still challenging. Because of multiple advanced features including flexibility, safety, and ease of manipulation, quadcopter drones have been widely adopted in many areas, from service improvement to urban surveillance, and data collection for scientific research. In this paper, a Smart Urban traffic Monitoring (SurMon) scheme is proposed employing drones following an edge computing paradigm. A dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision-making at the edge. Specifically, we propose to identify anomalous vehicle behaviors in real time by creatively applying the multidimensional Singular Spectrum Analysis (mSSA) technique in space to detect the different vehicle behaviors on roads. Multiple features of vehicle behaviors are fed into channels of the mSSA procedure. Instead of trying to create and define a database of normal activity patterns of vehicles on the road, the anomaly detection is reformatted as an outlier identifying problem. Then, a cascaded Capsules Network is designed to predict whether the behavior is a violation. An extensive experimental study has been conducted and the results have validated the feasibility and effectiveness of the SurMon scheme.

Enhanced drive‐by bridge modal identification via dual Kalman filter and singular spectrum analysis

The drive-by bridge health monitoring is to assess the bridge condition using the acceleration responses measured on the body or axle of instrumented vehicles. The vehicle responses are greatly affected by the road surface roughness that makes the bridge dynamic information blurred. Instead of direct using vehicle responses for the bridge monitoring, the dynamic response of contact point (CP) between the vehicle and bridge is further explored to enhance the drive-by bridge modal identification. A novel three-step framework is proposed to extract the components related to the bridge vibration from vehicle responses. The first step is to identify the input forces of two successive vehicles by solving the combined state-input estimation problem using dual Kalman filter. The CP responses of two contact points are calculated using the input forces and vehicle parameters. In the second step, the subtraction technique is applied to the identified CP responses of the two instrumented vehicles and the effect of the road surface roughness can be significantly reduced. Finally, an automatic singular spectrum analysis technique (auto SSA) is incorporated to decompose the response residual. Then the mono-component modes related to the bridge response are extracted from the response residual for drive-by bridge modal identification and/or the nonstationary characteristic identification of vehicle–bridge interaction (VBI) system. Results of numerical and experimental study demonstrate that the method can significantly suppress the vehicle response component and reduce the effect of road surface roughness to enhance the bridge modal identification.

Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction

In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods.

Forecasting of Air Maximum Temperature on Monthly Basis Using Singular Spectrum Analysis and Linear Autoregressive Model

In this research, the singular spectrum analysis technique is combined with a linear autoregressive model for the purpose of prediction and forecasting of monthly maximum air temperature. The temperature time series is decomposed into three components and the trend component is subjected for modelling. The performance of modelling for both prediction and forecasting is evaluated via various model fitness function. The results show that the current method presents an excellent performance in expecting the maximum air temperature in future based on previous recordings.

Singular Spectrum Analysis: Methodology and Comparison

In recent years Singular Spectrum Analysis (SSA), used as a powerful technique in time series analysis, has been developed and applied to many practical problems. In this paper, the performance of the SSA tech nique has been considered by applying it to a well-known time series data set, namely, monthly accidental deaths in the USA. The results are com pared with those obtained using Box-Jenkins SARIMA models, the ARAR algorithm and the Holt-Winter algorithm (as described in Brockwell and Davis (2002)). The results show that the SSA technique gives a much more accurate forecast than the other methods indicated above.

Eye-blink artifact removal from single channel EEG with k-means and SSA

In recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio (Gamma ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

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 Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.

Pre-processing Streamflow Data through Singular Spectrum Analysis with Fuzzy C-Means Clustering

One approach to improve water resource management is by making use of streamflow forecasts. In this study, eigenvector pairs were clustered by employing fuzzy c-means (FCM) during the grouping stage as an enhancement to the singular spectrum analysis (SSA) technique for data pre-processing. The FCM-SSA pre-processed streamflow data was then supplied to an auto-regressive integrated moving average (ARIMA) model for forecasting. The Department of Irrigation and Drainage Malaysia provided the monthly streamflow records of Sungai Muda (Jambatan Syed Omar) and Sungai Muda (Jeniang) for this research, wherein each was split into training (90%) and testing (10%) sets. The R software was the platform for building every FCM-SSA-ARIMA, SSA-ARIMA and ARIMA model, while the root mean squared errors and mean absolute errors were used to compare the performance between those models. The proposed FCM-SSA-ARIMA was discovered to be capable of surpassing the SSA-ARIMA and ARIMA models.

Singular spectrum analysis of GPS derived ionospheric TEC variations over Nepal during the low solar activity period

Accurate modeling of ionospheric total electron content (TEC) is an important aspect for mitigating the threats of trans-ionospheric delay error in satellite communication, earth observation, space-based navigation, timing applications as well as space weather forecasting services. In recent years, singular spectrum analysis (SSA) has been proved to be a powerful technique giving a relatively accurate estimate in time-series analysis comparable to the contemporary methods. In the current study, the SSA has been implemented on the GPS-derived TEC during the low solar activity year of 2017 over Nepal region which locates itself almost in the vicinity of low-latitudes being sandwiched between India and Tibet, China. The country foresees an explicit investigation and modeling of ionospheric TEC variations and corresponding delay error to precisely accomplish the space-based trans-ionospheric applications. The semi-annual variability of TEC with higher magnitudes during equinoctial seasons and lower values during solstice seasons is clearly noticed in the diurnal plots which are further substantiated by the trajectory matrix of time-series. The decomposed modes in the principal component analysis (PCA) signifies diurnal (first), semidiurnal (second), semiannual (third), monthly (fourth) with higher orders representing associated noise errors in the signals. Correlation coefficients (CC) between the reconstructed and observed time-series demonstrates the SSA method could be a successful tool for forecasting the TEC series over the region. The results are compared with empirical global ionospheric maps (GIMs) and IRI-Plas 2017 models during different seasons, emphasizing the suitability of SSA technique for relatively better precise TEC forecasting over the region.