Özet
Multivariable machine learning (ML) models are increasingly used for time series predictions. However, avoiding the overfitting and underfitting in ML-based time series prediction requires special consideration depending on the size and characteristics of the available training dataset. Predictive error compensating wavelet neural network (PEC-WNN) improves the time series prediction accuracy by enhancing the orthogonal features within a data fusion scheme. In this study, time series prediction performance of the PEC-WNNs have been evaluated on two different problems in comparison to conventional machine learning methods including the long short-term memory (LSTM) network. The results have shown that PECNET provides significantly more accurate predictions. RMSPE error is reduced by more than 60% with respect to other compared ML methods for Lorenz Attractor and wind speed prediction problems.
Orijinal dil | İngilizce |
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Sayfa (başlangıç-bitiş) | 1955-1963 |
Sayfa sayısı | 9 |
Dergi | TEM Journal |
Hacim | 10 |
Basın numarası | 4 |
DOI'lar | |
Yayın durumu | Yayınlandı - 2021 |
Bibliyografik not
Publisher Copyright:© 2021. TEM Journal. All Rights Reserved.
Finansman
This work was supported by the research project ‘‘Development of Cyberdroid Based on Cognitive Intelligent System Applications’’ (2019-2020) that is funded by Crypttech Inc within the contract by ITUNOVA-Istanbul Technical University Technology Transfer Office. Meteorological data has been provided from Agricultural Monitoring and Information Systems Project (TARBIL 2011A020100) supported by the Turkish Ministry of Development in cooperation with Ministry of Agriculture and Forestry in Turkey. This work was supported by the research project ?Development of Cyberdroid Based on Cognitive Intelligent System Applications?? (2019-2020) that is funded by Crypttech Inc within the contract by ITUNOVA Istanbul Technical University Technology Transfer Office. Meteorological data has been provided from Agricultural Monitoring and Information Systems Project (TARBIL 2011A020100) supported by the Turkish Ministry of Development in cooperation with Ministry of Agriculture and Forestry in Turkey.
Finansörler | Finansör numarası |
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Crypttech Inc | |
ITUNOVA Istanbul Technical University Technology Transfer Office | |
ITUNOVA-Istanbul Technical University | 2011A020100 |
Ministry of Agriculture and Forestry in Turkey |