forecasting

Time Series Clustering: Pattern Recognition, Forecasting, and Amortized Inference

Ángel López Oriona, Postdoctoral Research Fellow, Statistics

May 14, 12:00 - 13:00

B9 R2325

Time Series Pattern Recognition forecasting statistical inference

This talk presents innovative time series clustering techniques, highlighting a quantile-based approach for analyzing locally stationary data, a predictive framework for enhanced forecasting, and the use of amortized inference to overcome traditional algorithmic limitations.

Statistical Analysis of Multi-day Solar Irradiance using a Threshold Time Series model

Carolina D. Euan Campos, Postdoctoral Research Fellow, Statistics

Nov 28, 12:00 - 13:00

B9 H2 R2325

solar energy solar irradiance analysis nonlinearity TAR model statistical analysis forecasting

Abstract The analysis of solar irradiance has important applications in predicting solar energy production from solar power plants. Although the sun provides every day more energy than we need, the variability caused by environmental conditions affects electricity production. Most of the existing statistical models to forecast solar irradiance are linear and highly depend on normality assumptions. However, solar irradiance shows strong non-linearity and is only measured during the day time. Thus, we propose a new multi-day threshold autoregressive (TAR) model to quantify the variability of the