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 CRAN-published exdqlm R package for dynamic and static Bayesian quantile 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 1.0.0

CRAN release for Bayesian quantile modeling, with the companion JSS software article 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)