473 citations · 588 across the 23 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…
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…
Automated Imbalanced Classification via Layered Learning
Vitor Cerqueira, Luis Torgo, Paula Branco +1
In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach t…
AutoFITS: Automatic Feature Engineering for Irregular Time Series
Pedro Costa, Vitor Cerqueira, João Vinagre
A time series represents a set of observations collected over time. Typically, these observations are captured with a uniform sampling frequency (e.g. daily). When data points are…
STUDD: A Student-Teacher Method for Unsupervised Concept Drift Detection
Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet +1
Concept drift detection is a crucial task in data stream evolving environments. Most of state of the art approaches designed to tackle this problem monitor the loss of predictive m…
Evaluating time series forecasting models: An empirical study on performance estimation methods
Vitor Cerqueira, Luis Torgo, Igor Mozetic
Performance estimation aims at estimating the loss that a predictive model will incur on unseen data. These procedures are part of the pipeline in every machine learning project an…