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A time series forecasting based on cloud model similarity measurement

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Automation, Taiyuan University of Technology, Taiyuan, 030024, China

    Gaowei Yan, Songda Jia, Jie Ding, Xinying Xu & Yusong Pang

  2. Section Transport Engineering and Logistics, Delft University of Technology, 2628 CD, Delft, The Netherlands

    Yusong Pang

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  1. Gaowei Yan
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  2. Songda Jia
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  3. Jie Ding
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  4. Xinying Xu
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  5. Yusong Pang
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Correspondence to Yusong Pang.

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Yan, G., Jia, S., Ding, J. et al. A time series forecasting based on cloud model similarity measurement. Soft Comput 23, 5443–5454 (2019). https://doi.org/10.1007/s00500-018-3190-1

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