Authors :
Vaishnavi Punde; Shekhar Pawar
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/45hb665h
Scribd :
https://tinyurl.com/mrxbrrpf
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2377
Abstract :
Efficient resource allocation and strategic
planning in educational institutions heavily rely on
accurate predictions of student admissions. This paper
presents a detailed investigation into the application of
time series analysis techniques for admission prediction.
We explore the utilization of historical admission data,
spanning multiple academic cycles, to train and evaluate
several time series models.The study encompasses a
comparative analysis of traditional statistical models such
as autoregressive integrated moving average (ARIMA)
and seasonal autoregressive integrated moving average
(SARIMA) alongside more advanced deep learning
techniques like long short-term memory (LSTM)
networks. By evaluating the performance metrics,
including accuracy, robustness, and computational
efficiency, we aim to identify the most suitable model for
admission prediction tasks. Moreover, the research delves
into the impact of various external factors such as
changes in the academic calendar, socio-economic
indicators, and demographic shifts on admission patterns.
Understanding these factors is crucial for enhancing the
predictive capabilities of the models and enabling
institutions to adapt their strategies accordingly. The
experimental results and comparative analysis provide
valuable insights for educational institutions, enabling
them to make data-driven decisions regarding enrollment
management strategies and resource allocation.
Ultimately, this research contributes to the advancement
of admission prediction methodologies, facilitating more
efficient and informed decision-making processes in the
educational domain.
Keywords :
Time Series Analysis, ARIMA, SARIMA, Admission Forecasting, Educational Data Analysis.
References :
- Forecasting daily admissions to an emergency department considering single and multiple seasonal patterns.Adriana Vieira a, Inês Sousa a b, Sónia Dória-Nóbrega - https://www.sciencedirect.com/science/article/pii/S27724425 23000138
- Application of the ARIMA Model in Forecasting the Incidence of Tuberculosis in Anhui During COVID-19 Pandemic from 2021 to 2022 ,Shuangshuang Chen,Xinqiang Wang,Jiawen Zhao,Yongzhong Zhang &Xiaohong Kan - https://www.tandfonline.com/doi/full/10.2147/IDR.S367528
- Probability analysis and rainfall forecasting using ARIMA modelCHANDRAN S.,SELVAN P. ,KUMAR V., PRADEEPMISHRA https://mausamjournal.imd.gov.in/index.php/MAUSAM/arti cle/view/805
- Air pollutant prediction based on ARIMA-WOA-LSTM model panelJunLuo,,Yaping https://www.sciencedirect.com/science/article/pii/S13091042 23001150
- SALES FORECASTING USING ARIMA MODEL 1SandhyaC,2Dr.N.Radhakrishna naik
Efficient resource allocation and strategic
planning in educational institutions heavily rely on
accurate predictions of student admissions. This paper
presents a detailed investigation into the application of
time series analysis techniques for admission prediction.
We explore the utilization of historical admission data,
spanning multiple academic cycles, to train and evaluate
several time series models.The study encompasses a
comparative analysis of traditional statistical models such
as autoregressive integrated moving average (ARIMA)
and seasonal autoregressive integrated moving average
(SARIMA) alongside more advanced deep learning
techniques like long short-term memory (LSTM)
networks. By evaluating the performance metrics,
including accuracy, robustness, and computational
efficiency, we aim to identify the most suitable model for
admission prediction tasks. Moreover, the research delves
into the impact of various external factors such as
changes in the academic calendar, socio-economic
indicators, and demographic shifts on admission patterns.
Understanding these factors is crucial for enhancing the
predictive capabilities of the models and enabling
institutions to adapt their strategies accordingly. The
experimental results and comparative analysis provide
valuable insights for educational institutions, enabling
them to make data-driven decisions regarding enrollment
management strategies and resource allocation.
Ultimately, this research contributes to the advancement
of admission prediction methodologies, facilitating more
efficient and informed decision-making processes in the
educational domain.
Keywords :
Time Series Analysis, ARIMA, SARIMA, Admission Forecasting, Educational Data Analysis.
