29 citations · 58 across the 16 of their papers we have counts for
6 papers · 1 filter
An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures
Pablo Rodríguez-Bocca, Guillermo Pereira, Diego Kiedanski +3
In recent years, great progress has been made in the field of forecasting meteorological variables. Recently, deep learning architectures have made a major breakthrough in forecast…
Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation
Sebastian Basterrech
In Continual Learning (CL) contexts, concept drift typically refers to the analysis of changes in data distribution. A drift in the input data can have negative consequences on a l…
A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection
Sebastián Basterrech, Line Clemmensen, Gerardo Rubino
Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a m…
A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism
Radek Svoboda, Sebastian Basterrech, Jedrzej Kozal +2
Forecasting natural gas consumption, considering seasonality and trends, is crucial in planning its supply and consumption and optimizing the cost of obtaining it, mainly by indust…
Tracking changes using Kullback-Leibler divergence for the continual learning
Sebastián Basterrech, Michal Woźniak
Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of \emph{concept drift}, which consists of changing probabilistic ch…
An Empirical Study of the L2-Boost technique with Echo State Networks
Sebastián Basterrech
A particular case of Recurrent Neural Network (RNN) was introduced at the beginning of the 2000s under the name of Echo State Networks (ESNs). The ESN model overcomes the limitatio…