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20172021
most citedMachine Learning vs Statistical Methods for Time Series Forecasting: Size Matters

77 citations · 113 across the 13 of their papers we have counts for

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Showing 2021Show all

5 papers · 1 filter

cs.LG2021

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-…

stat.ML2021

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…

stat.ML2021

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…

cs.IR2021

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…

cs.LG2021★ 23 cited

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-…