473 citations · 588 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
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…
Forecasting with Deep Learning: Beyond Average of Average of Average Performance
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…
Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
José Leites, Vitor Cerqueira, Carlos Soares
Most forecasting methods use recent past observations (lags) to model the future values of univariate time series. Selecting an adequate number of lags is important for training ac…
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…
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…