1 citations · 1 across the 3 of their papers we have counts for
4 papers
Selective Time Series Forecasting via Metalearning
Ricardo Inácio, Vitor Cerqueira, Marília Barandas +1
Deep learning methods have achieved state-of-the-art in time series forecasting, yet their accuracy varies considerably across samples, as some instances remain inherently difficul…
Simulating Biases for Interpretable Fairness in Offline and Online Classifiers
Ricardo Inácio, Zafeiris Kokkinogenis, Vitor Cerqueira +1
Predictive models often reinforce biases which were originally embedded in their training data, through skewed decisions. In such cases, mitigation methods are critical to ensure t…
Meta-learning and Data Augmentation for Stress Testing Forecasting Models
Ricardo Inácio, Vitor Cerqueira, Marília Barandas +1
The effectiveness of univariate forecasting models is often hampered by conditions that cause them stress. A model is considered to be under stress if it shows a negative behaviour…
Time Series Data Augmentation as an Imbalanced Learning Problem
Vitor Cerqueira, Nuno Moniz, Ricardo Inácio +1
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different t…