Error-feedback Stochastic Configuration Strategy on Convolutional Neural Networks for Time Series Forecasting

-- Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture as well as the tuning of the hyper-parameters. Inspired by the iterative construction strategy for building a random multilayer perceptron, we propose a novel Error-feedback Stochastic Configuration (ESC) strategy to construct a random Convolutional Neural Network (ESC-CNN) for time series forecasting task, which builds the network architecture adaptively. The ESC strategy suggests that random filters and neurons of the error-feedback fully connected layer are incre-mentally added in a manner that they can steadily compensate the prediction error during the construction process, and a filter selection strategy is introduced to secure that ESC-CNN holds the universal approximation property, providing helpful information at each iterative process for the prediction. The performance of ESC-CNN is justified on its prediction accuracy for one-step- ahead and multi-step-ahead forecasting tasks. Comprehensive experiments on a synthetic dataset and two real-world datasets show that the proposed ESC-CNN not only outperforms the state-of-art random neural networks, but also exhibits strong predictive power in comparison to trained Convolution Neural Networks and Long Short-T erm Memory models, demonstrating the effectiveness of ESC-CNN in time series forecasting. Time series forecasting, especially computational intelligence enabled time series forecasting, is of great importance for a learning system in dynamic environments, and plays a vital role in applications such as in finance [1]-[3], energy [4]- [6], traffic [7]-[9], and electric load [10]-[12], etc. Recently, convolutional neural networks (CNNs) have been successfully implemented for time series forecasting tasks, benefiting from its strength in extracting local features via multiple convolu-tional filters and learning representation by fully connected layers [13]-[16].