Similarity measurement based on cloud models for time series prediction

Songda Jia, Xinying Xu, Yusong Pang, Gaowei Yan

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

3 Citations (Scopus)


Time series prediction has been extensively used for decision-making in many areas such as economics, engineering and medicine. And the useful data can be excavated by similarity measure of time series from a mass of historical data for predicting. The collected data from the real world is often uncertain, and the cloud model can be a good solution for the problem of uncertainty. This paper proposes a method based on the similarity degree of cloud model and combines it with back propagation network for prediction. In addition to the sequence itself, the trend of the sequence is used as another index of similarity. The neighbour set of query sequence from the training set is selected by similarity measure. Based on the neighbour set, a back propagation network is trained and used for prediction. Experimental results from the six time series show that the proposed method obtains better prediction accuracies than the comparative methods, which reveal its effectiveness.

Original languageEnglish
Title of host publicationProceedings of the 2016 28th Chinese Control and Decision Conference (CCDC 2016)
EditorsGuang-Hong Yang, Yuelin Gao, Huaguang Zhang
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-4673-9714-8
Publication statusPublished - 2016
EventCCDC 2016: 28th Chinese Control and Decision Conference - Yinchuan, China
Duration: 28 May 201630 May 2016


ConferenceCCDC 2016: 28th Chinese Control and Decision Conference


  • cloud model
  • neural network
  • similarity measure
  • time series prediction


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