Graduation Year
2022
Date of Submission
4-2022
Document Type
Open Access Senior Thesis
Degree Name
Bachelor of Arts
Department
Physics
Second Department
Mathematics
Reader 1
Julio Garin
Reader 2
Scot Gould
Terms of Use & License Information
Rights Information
© 2022 Abhinuv Uppal
Abstract
In many applications of graph analytics, the optimal graph construction is not always straightforward. I propose a novel algorithm to dynamically infer a graph structure on multiple time series by first imposing a state evolution equation on the graph and deriving the necessary equations to convert it into a maximum likelihood optimization problem. The state evolution equation guarantees that edge weights contain predictive power by construction. After running experiments on simulated data, it appears the required optimization is likely non-convex and does not generally produce results significantly better than randomly tweaking parameters, so it is not feasible to use in its current state. However, I discuss potential improvements and suggestions as to how the algorithm could become feasible in the future.
Recommended Citation
Uppal, Abhinuv, "Dynamic Nonlinear Gaussian Model for Inferring a Graph Structure on Time Series" (2022). CMC Senior Theses. 3005.
https://scholarship.claremont.edu/cmc_theses/3005
Data Repository Link
https://github.com/AbhiUppal/seniorthesis
Included in
Numerical Analysis and Computation Commons, Numerical Analysis and Scientific Computing Commons, Other Applied Mathematics Commons
