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Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering

Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering

Hua Ling Deng, Yǔ Qiàn Sūn
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 17
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781522566755|DOI: 10.4018/IJAEIS.2019100101
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MLA

Deng, Hua Ling, and Yǔ Qiàn Sūn. "Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering." IJAEIS vol.10, no.4 2019: pp.1-17. http://doi.org/10.4018/IJAEIS.2019100101

APA

Deng, H. L. & Sūn, Y. Q. (2019). Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 10(4), 1-17. http://doi.org/10.4018/IJAEIS.2019100101

Chicago

Deng, Hua Ling, and Yǔ Qiàn Sūn. "Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 10, no.4: 1-17. http://doi.org/10.4018/IJAEIS.2019100101

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Abstract

The high volatility of world soybean prices has caused uncertainty and vulnerability particularly in the developing countries. The clustering of time series is a serviceable tool for discovering soybean price patterns in temporal data. However, traditional clustering method cannot represent the continuity of price data very well, nor keep a watchful eye on the correlation between factors. In this work, the authors use the Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data (TICC) to soybean price pattern discovery. This is a new method for multivariate time series clustering, which can simultaneously segment and cluster the time series data. Each pattern in the TICC method is defined by a Markov random field (MRF), characterizing the interdependencies between different factors of that pattern. Based on this representation, the characteristics of each pattern and the importance of each factor can be portrayed. The work provides a new way of thinking about market price prediction for agricultural products.