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

Üstündağ B. B., Kulaglic A.

IEEE ACCESS, vol.8, pp.210532-210541, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 8
  • Publication Date: 2020
  • Doi Number: 10.1109/access.2020.3038724
  • Journal Name: IEEE ACCESS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.210532-210541
  • Istanbul Technical University Affiliated: Yes


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).