Time series forecasting based on deep extreme learning machine

Xuqi Guo, Yusong Pang, Gaowei Yan, Tiezhu Qiao

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

5 Citations (Scopus)
74 Downloads (Pure)

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).

Original languageEnglish
Title of host publicationProceedings of the 29th Chinese Control and Decision Conference (CCDC 2017)
EditorsGuang-Hong Yang, Dan Yang
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages6151-6156
ISBN (Electronic)978-1-5090-4656-0
DOIs
Publication statusPublished - 2017
Event29th Chinese Control and Decision Conference, CCDC 2017 - Chongqing, China
Duration: 28 May 201730 May 2017

Conference

Conference29th Chinese Control and Decision Conference, CCDC 2017
CountryChina
CityChongqing
Period28/05/1730/05/17

Bibliographical note

Accepted Author Manuscript

Keywords

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

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