Date of Submission
Open Access Senior Thesis
Bachelor of Arts
© 2022 Abhinuv Uppal
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.
Uppal, Abhinuv, "Dynamic Nonlinear Gaussian Model for Inferring a Graph Structure on Time Series" (2022). CMC Senior Theses. 3005.