In response, we propose a novel framework incorporated with feature attribution for detecting anomalies within multivariate time series data and then analyzing climate change trends in polar regions. Our methodology employs a Variational Autoencoder (VAE) framework, chosen for its stochastic nature, which we enhanced by incorporating correlation-based feature clustering and dynamic thresholding. These enhancements allow the VAE to focus on localized representations, thereby enriching the latent representation quality and the accuracy of detected anomalies. Given the complex interdependencies among variables and over time within multivariate climate data, we introduce the concepts of temporal overlap and proximity. These concepts allow us to identify how an anomaly in a variable relates to an anomaly in other variables. Through extensive experimentation on three distinct datasets, our research substantiates the efficacy of the proposed framework, marking a significant advancement in anomaly detection within climate data analysis
Image Note: In the left panel, Naomi Tack is defending her dissertation. In the right panel, Naomi stands with her iHARP mentors, (left to right: Dr. Becca Williams, Naomi Tack, and Dr. Don Engel).
Photo to the left: Pictured is Tolulope Ale defending his proposal.
Photo to the right: Pictured is Tolulope Ale standing with his committee. In the photo: Left of screen Left to Right: Dr. Vandana Janeja, Tolulope Ale
On Screen Top Right: Dr. Jianwu Wang, Bottom Left: Dr. Sudip Chakraborty, Bottom Right: Dr. Nicole-Jeanne Schegel
Right of Screen: Dr. Patti Ordóñez