ORCID
0000-0002-3714-2729
Keywords
Time Series Prediction, Time Series Analysis, Anomaly Detection, Text Classification, Transformers, Deep Learning
Abstract
Anomalies are rare in nature. This rarity makes it difficult for models to provide accurate and reliable predictions. Deep learning models typically excel at identifying underlying patterns from abundant data through supervised learning mechanisms but struggle with anomalies due to their limited representation. This results in a significant portion of errors arising from these rare and poorly represented events. Here, we present various methods and frameworks to develop the specialized ability of models to better detect and predict anomalies. Additionally, we improve the interpretability of these models by enhancing their anomaly awareness, leading to stronger performance on real-world datasets that often contain such anomalies. Because our models dynamically adapt to the significance of anomalies, they benefit from increased accuracy and prioritization of rare events in predictions. We demonstrate such capabilities on real-world datasets across multiple domains. Our results show that this framework enhances accuracy and interpretability, improving upon existing methods in anomaly prediction tasks.
Completion Date
2024
Semester
Fall
Committee Chair
Wang, Jun
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Department of Electrical and Computer Engineering
Degree Program
Computer Engineering
Format
Identifier
DP0028985
Language
English
Release Date
12-15-2024
Access Status
Dissertation
Campus Location
Orlando (Main) Campus
STARS Citation
Farhangi, Ashkan, "Adaptive Anomaly Prediction Models" (2024). Graduate Thesis and Dissertation post-2024. 23.
https://stars.library.ucf.edu/etd2024/23
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