The School of Computer Science is pleased to present…

Advanced Deep Learning Multivariate Multi-Time-Series Framework for a Novel COVID-19 Dataset

MSc Thesis Defense by: SWASTIK BAGGA

 

Date: Wednesday December 6, 2023

Time:  10:00 AM – 11:30 AM

Location: Essex Hall, Room 105

 

Abstract:

This thesis introduces an innovative framework aimed at addressing the complexities of predicting outcomes in multivariate multi time series datasets in regression analysis. By applying this framework to a novel COVID-19 dataset, it enhances predictive analytics by providing accurate forecasts for epidemic trends at regional or provincial levels, going beyond national-level analysis. The framework incorporates advanced data preprocessing, feature selection, engineering, encoding, and model architecture, effectively capturing intricate variable interactions and temporal dependencies. This makes it a powerful tool for tackling multivariate multi time series regression challenges, offering valuable insights for informed decision-making.

 Predicting outcomes in such datasets is challenging due to variable interconnections and temporal dynamics. The framework presented in the thesis adeptly models dependencies and latent patterns while considering real-world uncertainties. It demonstrates its practical value in localized epidemic trend forecasting, where deep data understanding is crucial for effective decision-making. Extensive experimentation shows that the framework outperforms traditional regression models and time series models in terms of various performance metrics, such as R^², MAE, MaxAE, and RMSE. A novel model, DeepAREstimator, is introduced to balance performance and training time, offering a maintainable and scalable solution for real-world applications.

The findings contribute to advancing predictive analytics, and providing essential insights for decision-making, particularly in localized epidemic trend forecasting.

 

Thesis Committee:

Internal Reader: Dr. Saeed Samet

External Reader: Dr. Ahmed Hamdi Sakr

Advisor: Dr. Ziad Kobti

Chair: Dr. Shafaq Khan