Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying

• Main Author: Liu , Zongying
• Format: Thesis
• Published: 2019
• Subjects: QA75 Electronic computers. Computer science; TJ Mechanical engineering and machinery
• Online Access: http://studentsrepo.um.edu.my/14823/2/Liu_Zongying.pdf; http://studentsrepo.um.edu.my/14823/1/Liu_Zongying.pdf; http://studentsrepo.um.edu.my/14823/

Multi-ahead time series prediction model is essential to human activities in the financial domain, electrical load, and natural disaster analysis/forecasting. Recent years, Machine correlation and potential non stationary of the data can be automatically analyzed. However, the problems with traditional offline and online learning algorithms in machine learning algorithms are usually faced with parameter dependency, concept drift handling problem, connectionless of neural net and unfixed reservoir. In this study, the proposed models consist of the off-line and on-line algorithm for time series prediction: Recurrent Kernel Extreme Reservoir Machine with Quantum Particle Swarm Optimization (RKERM-QPSO) and Meta-cognitive Recurrent Recursive Kernel On-line Sequential Extreme Learning Machine with the Drift Detection Machine (Meta-RRKOS-ELM-DDM) to solve above mentioned problems and improved overall prediction accuracy. In the forecasting process, the restriction of the prediction horizon is solved by the recurrent multi-steps-ahead algorithm for both off-line and on-line prediction models. The parameter dependency problem of the off-line and on-line model is handled with QPSO and new meta-cognitive learning strategy, respectively. Moreover, reservoir computing is applied to generate a fixed reservoir with high dimension information in the off-line prediction model in order to improve the forecasting accuracy. In the on-line learning model, the recursive kernel is also successfully used to generate a fixed reservoir with optimized information by dissipating and overwriting the information from the coming data in the learning part of the prediction model, which is helpful for improvement of forecasting performance. Besides, concept drift problem in on-line learning model is solved by Drift Detection Machine (DDM). In this study presents the theoretical insights which supporting the proposed arguments. The experiment is carried out by analyzed with benchmark data and a real-world example. In the proposed online learning model, the SMAPE result of 1-18 step periods in real-world data sets are 3.39% for S&P 500, 3.45% for Shanghai, and 5.48% for Ozone, respectively. It shown the super prediction ability compared to other state-of-the-art time-series-specific.