77 citations · 113 across the 13 of their papers we have counts for
14 papers
Pastprop-RNN: improved predictions of the future by correcting the past
André Baptista, Yassine Baghoussi, Carlos Soares +2
Forecasting accuracy is reliant on the quality of available past data. Data disruptions can adversely affect the quality of the generated model (e.g. unexpected events such as out-…
Model Compression for Dynamic Forecast Combination
Vitor Cerqueira, Luis Torgo, Carlos Soares +1
The predictive advantage of combining several different predictive models is widely accepted. Particularly in time series forecasting problems, this combination is often dynamic to…
Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators
Vitor Cerqueira, Luis Torgo, Carlos Soares
Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data where the observations show temporal dependenci…
u-cf2vec: Representation Learning for Personalized Algorithm Selection in Recommender Systems
Tomas Sousa-Pereira, Tiago Cunha, Carlos Soares
Collaborative Filtering (CF) has become the standard approach to solve recommendation systems (RS) problems. Collaborative Filtering algorithms try to make predictions about intere…
Promoting Fairness through Hyperparameter Optimization
André F. Cruz, Pedro Saleiro, Catarina Belém +2
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce. Existing methods are either metric-…
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
Vitor Cerqueira, Luis Torgo, Carlos Soares
The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a signif…