8 papers
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
Luis Amorim, Vitor Cerqueira, Moises Santos +2
Time series forecasting in privacy-sensitive domains often requires training models on released data rather than original observations. Synthetic time series generation has been de…
L-GTA: Latent Generative Modeling for Time Series Augmentation
Luis Roque, Vitor Cerqueira, Carlos Soares +1
Data augmentation is becoming increasingly important across various areas of time series analysis, including forecasting, classification, and anomaly detection. We introduce the La…
Selective Time Series Forecasting via Metalearning
Ricardo Inácio, Vitor Cerqueira, MarÃlia 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…
Grasynda: Graph-based Synthetic Time Series Generation
Luis Amorim, Moises Santos, Paulo J. Azevedo +2
Data augmentation is a crucial tool in time series forecasting, especially for deep learning architectures that require a large training sample size to generalize effectively. Howe…
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…
ModelRadar: Aspect-based Forecast Evaluation
Vitor Cerqueira, Luis Roque, Carlos Soares
Accurate evaluation of forecasting models is essential for ensuring reliable predictions. Current practices for evaluating and comparing forecasting models focus on summarising per…