20.500.12556/DKUM-91705 Long-term temperature prediction with hybrid autoencoder algorithms This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as input of a neural network, which will provide the final prediction output. Both approaches are tested in long-term air temperature prediction in European cities: seven European locations where major heat waves occurred have been considered. The longterm temperature prediction for the entire year of the heatwave events has been analysed. Results show that the proposed approaches can obtain accurate long-term (up to 4 weeks) temperature prediction, improving Persistence and Climatology in the benchmark models compared. In heatwave periods, where the persistence of the temperature is extremely high, our approach beat the persistence operator in three locations and works similarly in the rest of the cases, showing the potential of this AE-based method for long-term temperature prediction. autoencoder temperature prediction hybrid models heatwave avtoenkoderji napoved temperature hibridni modeli vročinski val true false true Elsevier Angleški jezik Slovenski jezik https://www.sciencedirect.com/journal/applied-computing-and-geosciences © 2024 The Author(s) Članek v reviji 2025-01-29 14:10:54 2025-01-29 14:10:55 2026-02-02 09:21:33 0000-00-00 00:00:00 2024 0 0 13 str. Vol. 23, [article no.] 100185 Sep. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-03-29 2024-08-01 2024-08-08 004.8 204807683 10.1016/j.acags.2024.100185 1873-6793 1-s2.0-S2590197424000326-main.pdf 1-s2.0-S2590197424000326-main.pdf 1 7DB04D9F78E0C33C9B8286C88A3C8EBF a8ad7c71207f8ef4725f3cee060a4eea34f11f24eb0891847bb6fa952dda49ff c5114695-de42-11ef-9dfa-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188281 https://www.sciencedirect.com/science/article/pii/S2590197424000326?via%3Dihub 0 3f45c29c-000f-11f1-90fc-00155d581219 https://dk.um.si/Dokument.php?lang=slv&id=202940 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0