6 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
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…
Kernel Corrector LSTM
Rodrigo Tuna, Yassine Baghoussi, Carlos Soares +1
Forecasting methods are affected by data quality issues in two ways: 1. they are hard to predict, and 2. they may affect the model negatively when it is updated with new data. The…