1 code implementation • 26 Jun 2023 • Vitor Cerqueira, Luis Torgo
On the other hand, the literature regarding the application of dynamic ensembles for multi-step ahead forecasting is scarce.
no code implementations • 20 Jun 2022 • Vitor Cerqueira, Luis Torgo
We frame the task of predicting extreme values of significant wave height as an exceedance probability forecasting problem.
no code implementations • 5 May 2022 • Vitor Cerqueira, Luis Torgo, Paula Branco, Colin Bellinger
The main contribution of our work is a new method called ICLL for tackling IBC tasks which is not based on resampling training observations.
1 code implementation • 29 Dec 2021 • Pedro Costa, Vitor Cerqueira, João Vinagre
We hypothesise that, in irregular time series, the time at which each observation is collected may be helpful to summarise the dynamics of the data and improve forecasting performance.
1 code implementation • 5 Apr 2021 • Vitor Cerqueira, Luis Torgo, Carlos Soares, Albert Bifet
In this paper, we leverage the idea of model compression to address this problem in time series forecasting tasks.
1 code implementation • 1 Apr 2021 • Vitor Cerqueira, Luis Torgo, Carlos Soares
We address this issue and compare a set of estimation methods for model selection in time series forecasting tasks.
1 code implementation • 1 Mar 2021 • Vitor Cerqueira, Heitor Murilo Gomes, Albert Bifet, Luis Torgo
In a set of experiments using 19 data streams, we show that the proposed approach can detect concept drift and present a competitive behaviour relative to the state of the art approaches.
1 code implementation • 22 Oct 2020 • Vitor Cerqueira, Luis Torgo, Carlos Soares
The early detection of anomalous events in time series data is essential in many domains of application.
3 code implementations • 14 Oct 2020 • Vitor Cerqueira, Nuno Moniz, Carlos Soares
Time series forecasting is a challenging task with applications in a wide range of domains.
1 code implementation • 29 Sep 2019 • Vitor Cerqueira, Luis Torgo, Carlos Soares
Using a learning curve method, our results suggest that machine learning methods improve their relative predictive performance as the sample size grows.
1 code implementation • 28 May 2019 • Vitor Cerqueira, Luis Torgo, Igor Mozetic
In this paper we address the application of these methods to time series forecasting tasks.
no code implementations • 14 Mar 2018 • Igor Mozetič, Luis Torgo, Vitor Cerqueira, Jasmina Smailović
Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data.