Anita Maria da Rocha Fernandes
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Anita Maria da Rocha Fernandes has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- David Cruz García
Expert Systems and Applications Laboratory, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
1 shared publication - Gabriel Villarrubia González
Expert Systems and Applications Laboratory, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
1 shared publication - Renan Otvin Klehm
Master in Applied Computing (MCA), Polytechnic School, University of Vale do Itajai (UNIVALI), Uruguai st. n.458, Itajai, 88302-901, Santa Catarina, Brazil, Brazil
1 shared publication - Rudimar Luis Scaranto Dazzi
University of Vale do Itajai
1 shared publication - Wemerson Delcio Parreira
Polytechnic School, Faculty of Electrical Engineering Pontifical Catholic University of Campinas Campinas Brazil
1 shared publication
Latest Publications
- Open Access
- 26 June 2025
- Published by MDPI AG in Preprints.org
A Hybrid Forecasting Model Using Statistical and Deep Neural Networks
The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others. We have proposed, in this paper, the use of statistical forecasters as covariates in a Deep Neural Network (DNN) model and evaluated its impact on forecast metrics. Our analysis covered four diverse datasets: M5, Stallion, Stock Market, and Synthetic. The results demonstrated that the inclusion of statistical predictors in the DNN model led to varying degrees of improvement in forecast performance, depending on the dataset and the evaluation metric chosen. In general, our findings suggest that the incorporation of statistical prediction as a covariate can be a valuable approach to improving multi-horizon prediction, especially in scenarios with data scarcity and intermittence.
- 26 June 2025
- Published by MDPI AG in Preprints.org
Open AccessA Hybrid Forecasting Model Using Statistical and Deep Neural Networks
The ability to accurately predict future time series behavior in multiple steps, known as multi-horizon forecasting, is a vital aspect in various industries, including retail sales, energy consumption, server load, healthcare, weather, and others. We have proposed, in this paper, the use of statistical forecasters as covariates in a Deep Neural Network (DNN) model and evaluated its impact on forecast metrics. Our analysis covered four diverse datasets: M5, Stallion, Stock Market, and Synthetic. The results demonstrated that the inclusion of statistical predictors in the DNN model led to varying degrees of improvement in forecast performance, depending on the dataset and the evaluation metric chosen. In general, our findings suggest that the incorporation of statistical prediction as a covariate can be a valuable approach to improving multi-horizon prediction, especially in scenarios with data scarcity and intermittence.