Antonio Aguirre Bayesian Forecasting | Quantile Modeling | Research Software

Antonio Aguirre

Antonio Aguirre

Bayesian forecasting, quantile modeling, and research software

I am a Ph.D. candidate in Statistics at the University of California, Santa Cruz. I work with Dr. Bruno Sansó and Dr. Raquel Prado on Bayesian time series forecasting and quantile modeling, with a focus on uncertainty quantification, scalable inference, and reproducible research software.

My current work includes Bayesian correction and synthesis of river-flow forecasts, Q-DESN quantile forecasting, and the exdqlm R package for extended dynamic quantile linear models.

I hold a B.Sc. in Applied Mathematics and an M.Sc. in Economics from Instituto Tecnológico Autónomo de México (ITAM).

Research Software CV
CRAN package

exdqlm 0.4.0

Bayesian dynamic quantile-modeling software on CRAN, with a companion JSS manuscript submitted.

Forecasting

River-flow correction

Bayesian quantile methods for correcting, combining, and evaluating hydrological forecasts.

Working paper

Q-DESN readouts

Bayesian quantile readouts for fixed Deep Echo State Network features.

Current Focus

  • Forecast correction and synthesis: methods that calibrate and combine probabilistic forecasts while preserving useful uncertainty information.
  • Dynamic quantile models: state-space models for conditional quantiles, forecast diagnostics, and posterior predictive summaries.
  • Q-DESN forecasting: Bayesian quantile readouts for fixed reservoir features in nonlinear time-series problems.
  • Research software: tested R, Python, C++, and workflow code for reproducible modeling and analysis.

Live Research Demo: San Lorenzo River Discharge

Note: This research display combines recent observed river flow at Big Trees with the latest forecast guidance. It is intended for model development and communication, not for operational safety decisions.
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Data sources: USGS NWIS IV and NOAA NWS/NWM APIs

Live Research Demo: GEFS Near Big Trees

Note: Updated automatically throughout the day. These charts use GEFS cycle-history context and the latest GEFS forecast range; external observed overlays are excluded so the comparison stays consistent.
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Precipitation Forecast

Recent precipitation and the expected forecast range for the coming days.

Soil Moisture Forecast

Recent soil moisture and forecast conditions by soil depth.

Data source: NOAA GEFS (AWS open data)