Antonio Aguirre Data Modeling | Bayesian Statistics | Machine Learning

Research Overview

I am a Ph.D. candidate in the Department of Statistics at the University of California, Santa Cruz, advised by Bruno Sansó and Raquel Prado. My research develops Bayesian time series methods for probabilistic forecasting, with an emphasis on quantile modeling, uncertainty quantification, and scalable inference.

Much of my work is motivated by environmental and hydrological data, but the methods are general and apply to other domains where forecasts must be transparent, efficient, and easy to update as new data arrive.

Current Projects

  • Quantile-Based Forecast Correction: Bayesian correction and synthesis of river flow forecasts using proper scoring rules.
  • Deep ESN Forecasting: Deep Echo State Networks for nonlinear dynamics and short-term prediction.
  • Scalable Inference: Variational Bayes and Sequential Monte Carlo for real-time updating in dynamic systems.

Selected Publications

  • Aguirre, A., Sansó, B., Prado, R. A Bayesian Quantile-Based Correction and Synthesis of River Flow Forecasts. Submitted to Environmetrics (Dec 2024).
  • Aguirre, A., Lobato, I.N. (2024). Evidence of Non-Fundamentalness in OECD Capital Stocks. Empirical Economics. [DOI]