Predictive Error Compensating Wavelet Neural Network Model for Multivariable Time Series Prediction

Ajla Kulaglic*, B. Berk Ustundag

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1955-1963
Number of pages9
JournalTEM Journal
Volume10
Issue number4
DOIs
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2021. TEM Journal. All Rights Reserved.

Funding

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.

FundersFunder number
Crypttech Inc
ITUNOVA Istanbul Technical University Technology Transfer Office
ITUNOVA-Istanbul Technical University2011A020100
Ministry of Agriculture and Forestry in Turkey

    Keywords

    • Multivariable time series prediction
    • Neural networks
    • Predictive error compensated wavelet neural networks
    • Spatial dimension
    • Time series prediction
    • Wavelet transform

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