Time series forecasting based on deep extreme learning machine
Xuqi Guo (Taiyuan University of Technology)
Y Pang (TU Delft - Transport Engineering and Logistics)
Gaowei Yan (Taiyuan University of Technology)
Tiezhu Qiao (Taiyuan University of Technology)
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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).