Time series forecasting based on deep extreme learning machine

Time series forecasting based on deep extreme learning machine

Guo, Xuqi (Taiyuan University of Technology)
Pang, Y. (TU Delft Transport Engineering and Logistics)
Yan, Gaowei (Taiyuan University of Technology)
Qiao, Tiezhu (Taiyuan University of Technology)

Yang, Guang-Hong (editor)
Yang, Dan (editor)

2017

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in which the hybrid Euclidean distance is used as the similarity measurement between two sets of time series. In order to improve the efficiency, prediction performance, as well as the ability of real-time updating of the model, in this paper, the recombination samples of the model is derived by Deep Extreme Learning Machine (DELM). The experiments show that local prediction model gets accurate results in one-step and multi-step forecasting, and the model has good generalization performance through the test on the five data sets selected from Time Series Database Library (TSDL).

Deep Extreme Learning Machine
Hybrid Euclidean distance
Local model
Time series prediction

http://resolver.tudelft.nl/uuid:f11a298e-637a-4bf4-8801-2f9aae785cc1

https://doi.org/10.1109/CCDC.2017.7978277

IEEE, Piscataway, NJ, USA

978-1-5090-4656-0

Proceedings of the 29th Chinese Control and Decision Conference (CCDC 2017)

29th Chinese Control and Decision Conference, CCDC 2017, 2017-05-28 → 2017-05-30, Chongqing, China

Accepted Author Manuscript

Institutional Repository

conference paper

© 2017 Xuqi Guo, Y. Pang, Gaowei Yan, Tiezhu Qiao