Comparison of singular spectrum analysis forecasting algorithms for student’s academic performance during COVID-19 outbreak

Published on Jan 31, 2021
· DOI :10.19101/IJATEE.2020.S1762138
Muhammad Fakhrullah Mohd Fuad1
Estimated H-index: 1
,
Shazlyn Milleana Shaharudin4
Estimated H-index: 4
+ 2 AuthorsMuhammad Fareezuan Zulfikri1
Estimated H-index: 1
Sources
Abstract
Due to the spread of COVID-19 that hit Malaysia, all academic activities at educational institutions including universities had to be carried out via online learning However, the effectiveness of online learning is remains unanswered Besides, online learning may have a significant impact if continued in the upcoming academic sessions Therefore, the core of this study is to predict the academic performance of undergraduate students at one of the public universities in Malaysia by using Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) and Vector Forecasting-Singular Spectrum Analysis (VF-SSA) The key concept of the predictive model is to improve the efficiency of different types of forecast model in SSA by using two parameters which are window length (L) and number of leading components (r) The forecasting approaches in SSA model was based on the Grading Point Assessments (GPA) for undergraduate students from Faculty Science and Mathematics, UPSI via online classes during COVID-19 outbreak The experiment revealed that parameter L= 11 (T/20) has the best prediction result for RF-SSA model with RMSE value of 0 19 as compared to VF-SSA of 0 30 This signifies the competency of RF-SSA in predicting the students’ academic performances based on GPA for the upcoming semester Nonetheless, an RF-SSA algorithm should be developed for higher effectivity of obtaining more data sets including more respondents from various universities in Malaysia © 2021 Muhammad Fakhrullah Mohd Fuad et al
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The impact of coronavirus disease 2019 on radiology training programs has been profound and continues to increase as case counts rise; the authors of this report provide an overview of major proble...
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Wind speed forecasting helps to increase the efficacy of wind farms and prompts the comparative superiority of wind energy in the global electricity system. Many wind speed forecasting theories have been widely applied to forecast wind speed, which is nonlinear, and unstable. Current forecasting strategies can be applied to various wind speed time series. However, some models neglect the prerequisite of data preprocessing and the objective of simultaneously optimizing accuracy and stability, whi...
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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 extracte...
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