Date of Award


Publication Type


Degree Name



Computer Science


COVID-19 epidemic prediction;Deep Learning;Machine Learning;Multi Variate Multi Timeseries Dataset;Multi Variate Time Series;Time Series


Ziad Kobti



Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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.