6 citations · 6 across the 6 of their papers we have counts for
6 papers · 1 filter
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
Ricardo Matos, Luis Roque, Vitor Cerqueira
Deep learning approaches are increasingly relevant for time series forecasting tasks. Methods such as N-BEATS, which is built on stacks of multilayer perceptrons (MLPs) blocks, hav…
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…
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…
Cherry-Picking in Time Series Forecasting: How to Select Datasets to Make Your Model Shine
Luis Roque, Carlos Soares, Vitor Cerqueira +1
The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through e…
RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms
Luis Roque, Carlos Soares, Luís Torgo
We introduce the Robustness of Hierarchically Organized Time Series (RHiOTS) framework, designed to assess the robustness of hierarchical time series forecasting models and algorit…
Online Data Augmentation for Forecasting with Deep Learning
Vitor Cerqueira, Moisés Santos, Luis Roque +2
Deep learning approaches are increasingly used to tackle forecasting tasks involving datasets with multiple univariate time series. A key factor in the successful application of th…