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A time series forecasting based on cloud model similarity measurement

  • Gaowei Yan
  • Songda Jia
  • Jie Ding
  • Xinying Xu
  • Yusong Pang
Methodologies and Application

Abstract

In this paper, a local cloud model similarity measurement (CMSM) is proposed as a novel method to measure the similarity of time series. Time series similarity measurement is an indispensable part for improving the efficiency and accuracy of prediction. The randomness and uncertainty of series data are critical problems in the processing of similarity measurement. CMSM obtains the internal information of time series from the general perspective and local trend using the cloud model, which reduces the uncertainty of measurement. The neighbor set is selected from time series by CMSM and used to construct a prediction model based on least squares support vector machine. The proposed technique reduces the potential for overfitting and uncertainty and improves model prediction quality and generalization. Experiments were performed with four datasets selected from Time Series Data Library. The experimental results show the feasibility and effectiveness of the proposed method.

Keywords

Time series forecasting Similarity measurement Cloud model Uncertainty Least squares support vector machine 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (61450011) and Natural Science Foundation of Shanxi (2015011052).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Gaowei Yan
    • 1
  • Songda Jia
    • 1
  • Jie Ding
    • 1
  • Xinying Xu
    • 1
  • Yusong Pang
    • 1
    • 2
  1. 1.Department of AutomationTaiyuan University of TechnologyTaiyuanChina
  2. 2.Section Transport Engineering and LogisticsDelft University of TechnologyDelftThe Netherlands

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