473 citations · 588 across the 23 of their papers we have counts for
25 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…
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…
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…
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…
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…
Multi-output Ensembles for Multi-step Forecasting
Vitor Cerqueira, Luis Torgo
This paper studies the application of ensembles composed of multi-output models for multi-step ahead forecasting problems. Dynamic ensembles have been commonly used for forecasting…
Exceedance Probability Forecasting via Regression for Significant Wave Height Prediction
Vitor Cerqueira, Luis Torgo
Significant wave height forecasting is a key problem in ocean data analytics. This task affects several maritime operations, such as managing the passage of vessels or estimating t…
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…
Model Compression for Dynamic Forecast Combination
Vitor Cerqueira, Luis Torgo, Carlos Soares +1
The predictive advantage of combining several different predictive models is widely accepted. Particularly in time series forecasting problems, this combination is often dynamic to…
Model Selection for Time Series Forecasting: Empirical Analysis of Different Estimators
Vitor Cerqueira, Luis Torgo, Carlos Soares
Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data where the observations show temporal dependenci…
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…
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes
Vitor Cerqueira, Luis Torgo, Carlos Soares
The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a signif…
VEST: Automatic Feature Engineering for Forecasting
Vitor Cerqueira, Nuno Moniz, Carlos Soares
Time series forecasting is a challenging task with applications in a wide range of domains. Auto-regression is one of the most common approaches to address these problems. Accordin…
Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters
Vitor Cerqueira, Luis Torgo, Carlos Soares
Time series forecasting is one of the most active research topics. Machine learning methods have been increasingly adopted to solve these predictive tasks. However, in a recent wor…
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…
How to evaluate sentiment classifiers for Twitter time-ordered data?
Igor Mozetič, Luis Torgo, Vitor Cerqueira +1
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focu…