|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|84065||158859||2016||7 صفحه PDF||سفارش دهید||دانلود رایگان|
• We perform a model to study multidimensional and temporal data.
• Described a methodology for spatio-temporal trajectories analysis.
• Application using Tucker decomposition and tucker3 model.
• Analysis applied in agrieconomics time series databases.
• We explore analyses in real databases of grain prices indexes.
Agribusiness is an activity that generates huge amounts of temporal data. There are research centers that collect, store and create indexes of agricultural activities, providing multidimensional time series composed by years of data. In this paper, we are interested in studying the behavior of these time series, especially in what regards the evolution of agricultural price indexes over the years. We explore data mining techniques tailored to analyze temporal data, aiming to generate spatio-temporal trajectories of grains price indexes for six years of data. We propose the use of Tucker decomposition to both analyze the temporal patterns of these price indexes and map trajectories that represent their behavior over time in a concise and representative low-dimensional subspace. The case study presents an application of this methodology to real databases of price indexes of corn and soybeans in Brazil and the United States.
Journal: Computers and Electronics in Agriculture - Volume 120, January 2016, Pages 72–78