High-Performance Time Series Prediction with Predictive Error Compensated Wavelet Neural Networks

Burak Berk Ustundag, Ajla Kulaglic*

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

34 Atıf (Scopus)

Özet

Machine learning (ML) algorithms have gained prominence in time series prediction problems. Depending on the nature of the time series data, it can be difficult to build an accurate ML model with the proper structure and hyperparameters. In this study, we propose a predictive error compensation wavelet neural network model (PEC-WNN) for improving the prediction accuracy of chaotic and stochastic time series data. In the proposed model, an additional network is used for the prediction of the main network error to compensate the overall prediction error. The main network takes as inputs the time series data through moving frames in multiple-scales. The same structure and hyperparameter sets are applied for quite distinct four types of problems for verification of the robustness and accuracy of the proposed model. Specifically, the Mackey-Glass, Box-Jenkins, and Lorenz Attractor benchmark problems, as well as drought forecasting are used to characterize the performance of the model for chaotic and stochastic data cases. The results show that the PEC-WNN provides significantly more accurate predictions for all compared benchmark problems with respect to conventional machine learning and time series prediction methods without changing any hyperparameter or the structure. In addition, the time and space complexity of the PEC-WNN model is less than all other compared ML methods, including long short-term memory (LSTM) and convolutional neural networks (CNNs).

Orijinal dilİngilizce
Makale numarası9269330
Sayfa (başlangıç-bitiş)210532-210541
Sayfa sayısı10
DergiIEEE Access
Hacim8
DOI'lar
Yayın durumuYayınlandı - 2020

Bibliyografik not

Publisher Copyright:
© 2013 IEEE.

Finansman

This work was supported by the research project ‘‘Development of Cyberdroid Based on Cognitive Intelligent System Applications’’ (2019-2020) funded by Crypttech company within the contract by ITUNOVA-Istanbul Technical University Technology Transfer Office.

FinansörlerFinansör numarası
ITUNOVA-Istanbul Technical University

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