IMPROVING MULTI-VARIATE TIME SERIES FORECASTING WITH DYNAMIC MULTI-HEAD ATTENTION ADJACENCY MATRIX
dc.contributor.advisor | Dr. Qin Ding | |
dc.contributor.author | Bruce, Ashley Denise | |
dc.contributor.committeeMember | Dr. Sinan Sousan | |
dc.contributor.committeeMember | Dr. Nic Herndon | |
dc.contributor.department | Computer Science | |
dc.date.accessioned | 2025-06-05T17:30:22Z | |
dc.date.available | 2025-06-05T17:30:22Z | |
dc.date.created | 2025-05 | |
dc.date.issued | May 2025 | |
dc.date.submitted | May 2025 | |
dc.date.updated | 2025-05-22T21:15:04Z | |
dc.degree.college | College of Engineering and Technology | |
dc.degree.grantor | East Carolina University | |
dc.degree.major | MS-Data Science | |
dc.degree.name | M.S. | |
dc.degree.program | MS-Data Science | |
dc.description.abstract | Time series data is prevalent in many fields, such as finance, weather forecasting, and economics. Predicting future values of a time series can offer valuable insights for decision-making, such as identifying trends, detecting anomalies, and improving resource allocation. Existing research, including neural network-based models and transformer-based models, has demonstrated high performance in learning temporal information. However, capturing spatial information within time series data remains a significant challenge. In this project, we explored whether the attention mechanism can be effectively integrated into non-transformer-based models to enhance their ability to learn spatial information. To achieve this goal, we propose a novel framework that uses a dynamically learned adjacency matrix based on related work called the Multi-variate Time-series Graph Neural Network(MTGNN). Instead of using a correlation-based learned adjacency matrix, the adjacency matrix and graph modules are replaced with a dynamically learned adjacency matrix with multi-attention. This framework shows that a dynamically learned attention adjacency matrix can perform as well as other frameworks when learning spatial information. | |
dc.format.mimetype | application/pdf | |
dc.identifier.uri | http://hdl.handle.net/10342/14041 | |
dc.publisher | East Carolina University | |
dc.subject | Statistics | |
dc.subject | Computer Science | |
dc.title | IMPROVING MULTI-VARIATE TIME SERIES FORECASTING WITH DYNAMIC MULTI-HEAD ATTENTION ADJACENCY MATRIX | |
dc.type | Master's Thesis | |
dc.type.material | text |