Robust Adaptive Rescaled Lncosh Neural Network Regression Toward Time-Series Forecasting
Yang Yang, Hu Zhou, Jinran Wu, Zhe Ding, Yu‐Chu Tian, Dong Yue, You‐Gan Wang · IEEE Transactions on Systems Man and Cybernetics Systems · 2023
In time series forecasting with outliers and random noise, parameter estimation in a neural network via minimizing the$l_{2}$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$l_{1}$,$l_{2}$, 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.