Abstract
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 language | English |
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Title of host publication | Proceedings of the 2016 28th Chinese Control and Decision Conference (CCDC 2016) |
Editors | Guang-Hong Yang, Yuelin Gao, Huaguang Zhang |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 5138-5142 |
ISBN (Electronic) | 978-1-4673-9714-8 |
DOIs | |
Publication status | Published - 2016 |
Event | CCDC 2016: 28th Chinese Control and Decision Conference - Yinchuan, China Duration: 28 May 2016 → 30 May 2016 |
Conference
Conference | CCDC 2016: 28th Chinese Control and Decision Conference |
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Country/Territory | China |
City | Yinchuan |
Period | 28/05/16 → 30/05/16 |
Keywords
- cloud model
- neural network
- similarity measure
- time series prediction