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.
Highlights
The soybean is one of the requisite grain crops in the world, and has been cultivated for more than 5,000 years
Each pattern in the Toeplitz Inverse Covariance-based Clustering (TICC) method is defined by a Markov random field (MRF), characterizing the interdependencies between different factors of that pattern
In Hallac’s literature, TICC method has shown good performance when compared with several state-of-the-art baselines such as Gaussian Mixture Model (GMM) and Dynamic Time Warping (DTW)
Summary
The soybean is one of the requisite grain crops in the world, and has been cultivated for more than 5,000 years. A clustering methodology based on Toeplitz matrix is proposed for discover the agricultural products market price pattern. Compared with the traditional clustering, TICC is a new type of modelbased multivariate time series clustering method, which can find the accurate and interpretable structure in the data. The total number of segments is defined by the smoothness parameter “ β ” It does so by running an EM algorithm where TICC alternately assigns points to clusters using a DP algorithm and updates the cluster parameters by solving a Toeplitz inverse covariance estimation problem. The solver takes as input a T-by-n data matrix, the window size “w”, the number of clusters “k” and some necessary parameters, returns an array of cluster assignments for each time point. We will demonstrate our experiment and analysis in two sections
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