Price spike forecasting using concept-tree approach based on cloud model

Weng, Yingjun, Shi, Laide and Zhao, Jun Hua (2009). Price spike forecasting using concept-tree approach based on cloud model. In: Qi Luo, Jiang Yi and Chen Bin, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management. 2009 2nd ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009, Sanya, China, (352-355). 8 - 9 August 2009. doi:10.1109/CCCM.2009.5267930


Author Weng, Yingjun
Shi, Laide
Zhao, Jun Hua
Title of paper Price spike forecasting using concept-tree approach based on cloud model
Conference name 2009 2nd ISECS International Colloquium on Computing, Communication, Control, and Management, CCCM 2009
Conference location Sanya, China
Conference dates 8 - 9 August 2009
Proceedings title 2009 ISECS International Colloquium on Computing, Communication, Control, and Management
Place of Publication Piscataway, NJ United States
Publisher I E E E
Publication Year 2009
Year available 2009
Sub-type Fully published paper
DOI 10.1109/CCCM.2009.5267930
Open Access Status
ISBN 9781424442461
9781424442478
Editor Qi Luo
Jiang Yi
Chen Bin
Volume 2
Start page 352
End page 355
Total pages 4
Language eng
Abstract/Summary There are many techniques for electricity market price forecasting. The challenge of spike prediction is the accuracy of the prediction that is on how a classifier can capture all spikes that would happen. In this paper, we introduce a novel data discretization approach using cloud models to implement concept hierarchies and data reduction. An effective framework of predicting the occurrence of spikes has been discussed in details. A concept-tree approach based on cloud model is presented to give a reliable forecast of the occurrence of price spikes with low dimension space and automated concept level. Combined with the spike value prediction techniques, the proposed approach aims at providing a comprehensive tool for price spike forecasting are discussed in detail. Realistic market data are used to test the proposed model with promising results.
Subjects 1703 Computational Theory and Mathematics
1705 Computer Networks and Communications
1706 Computer Science Applications
1712 Software
Keyword Cloud model
Concept-treeapproach
Price spike forecast
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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