About Ángel López Oriona Ángel López Oriona Postdoctoral Research Fellow, Statistics computational statistics Time Series Data Mining artificial intelligence Events Presented Events May 10 - May 16, 2026 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. Feb 11 - Feb 17, 2024 Fuzzy clustering of circular time series based on a new distance with applications to wind data Ángel López Oriona, Postdoctoral Research Fellow, Statistics Feb 15, 12:00 - 13:00 B9 L2 H2 H2 Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real line, very few works consider time series defined on the unit circle, although the latter objects frequently arise in many applications. In this talk, the problem of clustering circular time series is discussed.
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.
Fuzzy clustering of circular time series based on a new distance with applications to wind data Ángel López Oriona, Postdoctoral Research Fellow, Statistics Feb 15, 12:00 - 13:00 B9 L2 H2 H2 Time series clustering is an essential machine learning task with applications in many disciplines. While the majority of the methods focus on time series taking values on the real line, very few works consider time series defined on the unit circle, although the latter objects frequently arise in many applications. In this talk, the problem of clustering circular time series is discussed.
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