Publications (19)
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…
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…
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…
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…
How to evaluate sentiment classifiers for Twitter time-ordered data?
Igor MozetiÄ, 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…
Beyond Average Performance -- exploring regions of deviating performance for black box classification models
Luis Torgo, Paulo Azevedo, Ines Areosa
Machine learning models are becoming increasingly popular in different types of settings. This is mainly caused by their ability to achieve a level of predictive performance that i…
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…
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…
A Survey of Predictive Modelling under Imbalanced Distributions
Paula Branco, Luis Torgo, Rita Ribeiro
Many real world data mining applications involve obtaining predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least co…
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…
Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation
Mohammad Etemad, Zahra Etemad, Amilcar Soares +3
Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for…
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…
OpenML: networked science in machine learning
Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl +1
Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In thi…
A Survey on Spatio-temporal Data Analytics Systems
Md Mahbub Alam, Luis Torgo, Albert Bifet
Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to…
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…
UBL: an R package for Utility-based Learning
Paula Branco, Rita P. Ribeiro, Luis Torgo
This document describes the R package UBL that allows the use of several methods for handling utility-based learning problems. Classification and regression problems that assume no…
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…
An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R
Luis Torgo
This document describes an infra-structure provided by the R package performanceEstimation that allows to estimate the predictive performance of different approaches (workflows) to…