Résumé
We use transfer learning in order to reduce the computational time of our approach. We have compared results obtained with other state of the art time series forecasting techniques on twenty time series, which contains data generated by sensors placed on a number of Iberian pigs. Results obtained confirm the effectiveness of the strategy proposed in this work.
Overall, we showcase the potential of our proposal in producing precise and efficient deep learning models for time series prediction, as well as the adaptability of transfer learning to new datasets.
langue originale | Anglais |
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Pages | 219-228 |
Nombre de pages | 10 |
Les DOIs | |
Etat de la publication | Publié - 31 août 2023 |
Financement
Acknowledgements. Iztok Fister Jr. is grateful the Slovenian Research Agency for the financial support under Research Core Funding No. P2-0057. Iztok Fister thanks the Slovenian Research Agency for the financial support under Research Core Funding No. P2-0042 - Digital twin. This research is also supported by the PDE-GIR project from the European Union Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No 778035, and the project PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU. This research work has been supported by the Spanish national project PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU, and the European project PDE-GIR with grant number H2020-MSCA-RISE-2017-778035 of the European Union’s Horizon 2020 research and innovation programme, Marie Sklodowska-Curie Actions (MSCA) programme. Acknowledgement. This work was partially supported by the European Commission, under European Project 5G-Induce, grant number 101016941. This work is partially supported by Universidad de León, under the “Programa Propio de Investigación de la Universidad de León 2021” grant. Acknowledgements. This research is supported by the project Future Artificial Intelligence Research (FAIR) - PNRR MUR Cod. PE0000013 - CUP: E63C22001940006. Acknowledgements. The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the projects PID2020-117954RB-C22 and TED2021-131311B and Junta de Andalućıa for the project PYC20 RE 078 USE. Acknowledgments. The authors would like to acknowledge the financial support of the Ministerio de Ciencias, Tecnología e InnovaciÃşn (Minciencias) through Scholarship Program No. 860. This work has also benefited from a State grant managed by the National Research Agency under the “Investissements d’Avenir” program with the reference ANR-18-EURE-0021. Work also partially supported by the Spanish project TED2021-132470B-I00, funded by MCIN-AEI-10.13039-501100011033, and the GOMINOLA project (PID2020-118112RB-C21, funded by MCIN-AEI-10.13039-501100011033). CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01). Acknowledgements. The authors would like to thank the Spanish Ministry of Science and Innovation and the Junta de Andalucía for their support within the projects PID2020-117954RB-C21 and TED2021-131311B-C22, PY20-00870, UPO-138516, respectively. The authors would also like to thank the Fundación Tatiana Pérez de Guzmán el Buenofor the support offered through the Beca Predoctoral en Medioambiente de 2018. Acknowledgements. Míriam Timiraos’s research was supported by the “Xunta de Galicia” (Regional Government of Galicia) through grants to industrial PhD (http:// gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04 IN606D 2022 2692965. Acknowledgments. Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the ”Formación de Profesorado Universitario” grant with reference FPU21/00932. Particular thanks go as well to the conference’s main sponsors, Startup Olé, the CYL-HUB project financed with NEXT-GENERATION funds from the European Union and channeled by Junta de Castilla y León through the Regional Ministry of Industry, Trade and Employment, BISITE research group at the University of Salamanca, CTC research group at the University of A Coruña, and the University of Salamanca. They jointly contributed in an active and constructive manner to the success of this initiative. Acknowledgements. Authors acknowledge funding under grant AI4TES, by the Spanish Min. of Economic Affairs and Digital Transformation and by EU Next Generation EU/PRTR. Carlos J. Gallego acknowledges funding for his scholarship from UPM RP180022025. Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universi-tario” grant with reference: FPU21/00932. Acknowledgments. The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the project PID2020-117954RB-C21 and the European Regional Development Fund and Junta de Andalućıa for projects PY20-00870 and UPO-138516. Míriam Timiraos’s research was supported by the Xunta de Galicia (Regional Government of Galicia) through grants to industrial Ph.D. (http://gain.xunta.gal), under the Doutoramento Industrial 2022 grant with reference: 04 IN606D 2022 2692965.
Bailleurs de fonds | Numéro du bailleur de fonds |
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ANR-18-EURE-0021 | TED2021-132470B-I00, MCIN-AEI-10.13039-501100011033, PID2020-118112RB-C21 |
Consellería de Educación, Universidade e Formación | |
FAIR | E63C22001940006, PE0000013 |
Formación de Profesorado Universi-tario | |
Marie Skodowska-Curie | 778035, PID2021-127073OB-I00 |
Regional Ministry of Industry, Trade and Employment | |
Secretaría Xeral de Universidades | ED431G 2019/01 |
Spanish Ministry of Universities | FPU21/00932 |
Universidad de León 2021 | |
Universidad de Salamanca | |
Xunta de Galicia (Regional Government of Galicia | |
Horizon 2020 Framework Programme | |
HORIZON EUROPE Marie Sklodowska-Curie Actions | |
European commission | H2020-MSCA-RISE-2017-778035, 101016941 |
Agence Nationale de la Recherche | |
Ministerio de Ciencia, Tecnología e Innovación | 860 |
Javna Agencija za Raziskovalno Dejavnost RS | P2-0057, P2-0042 |
Universiti Putra Malaysia | |
Ministerio de Ciencia e Innovación | TED2021-131311B, PID2020-117954RB-C22 |
Horizon 2020 | |
Consejería de Educación, Junta de Castilla y León | |
European Regional Development Fund | |
Xunta de Galicia | 04 IN606D 2022 2692965 |
Junta de Andalucía | PY20-00870, PID2020-117954RB-C21, TED2021-131311B-C22, PYC20 RE 078 USE, UPO-138516 |
Universidad de León | |
project Future Artificial Intelligence Research |