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Robust adaptive rescaled lncosh neural network regression toward time-series forecasting
Yang, Yang Zhou, Hu Wu, Jinran Ding, Zhe Tian, Yu-Chu Yue, Dong Wang, You-Gan
Yang, Yang
Zhou, Hu
Wu, Jinran
Ding, Zhe
Tian, Yu-Chu
Yue, Dong
Wang, You-Gan
Abstract
In time series forecasting with outliers and random noise, parameter estimation in a neural network via minimizing the l2 loss is unreliable. Therefore, an adaptive rescaled lncosh loss function is proposed in this article to handle time series modeling with outliers and random noise. It overcomes the limitation of the single distribution of traditional loss functions and can switch among l1 , l2 , and the Huber losses. A tuning parameter in the loss function is estimated by using a “working” likelihood approach according to estimated residuals. From the proposed loss function, a robust adaptive rescaled lncosh neural network (RARLNN) regression model is developed for highly accurate predictions. In the training phase of the model, an iterative learning procedure is presented to estimate the tuning parameter and train the neural network in iterations. A new prediction interval construction method is also developed based on quantile theory. The proposed RARLNN model is applied to two groups of wind speed forecasting tasks. The results show that the proposed RARLNN model is more conducive to enhancing forecasting accuracy and stability from the perspectives of noise distribution and outliers.
Keywords
outliers, prediction interval (PI), robust loss function, time series forecasting (TSF)
Date
2023
Type
Journal article
Journal
IEEE Transactions on Systems, Man and Cybernetics: Systems
Book
Volume
53
Issue
9
Page Range
5658-5669
Article Number
ACU Department
Institute for Positive Psychology and Education
Faculty of Education and Arts
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Institute for Learning Sciences and Teacher Education (ILSTE)
Relation URI
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled