Time series forecasting based on deep extreme learning machine

Conference paper (2017)

Authors

Xuqi Guo Taiyuan University of Technology
Y. Pang Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Gaowei Yan Taiyuan University of Technology
Tiezhu Qiao Taiyuan University of Technology
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 Xuqi Guo, Y. Pang, Gaowei Yan, Tiezhu Qiao
DOI
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https://doi.org/10.1109/CCDC.2017.7978277
Time series prediction Deep Extreme Learning Machine Hybrid Euclidean distance Local model
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

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).