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A comprehensive method for medium-term analysis and forecasting (CMAF) of global monthly prices of agricultural commodities

CORDIS · observation · Publication date unknown

modities. The project will create a tool based on this methodology, which provides a detailed explanation of the forecasts and enables their complete interpretation. Integrating eight econometric and machine learning (ML) methods, CMAF will combine the joint effects of over 100 possible variables. In addition, it will consider the inclusion of additional potential explanators for specific needs or purposes. It will use different cross-validation techniques to avoid a priori research assumptions and realistically captures these complex relations. First, the learning process begins with comprehensive stationary and causality tests, which detect the nature of each possible variable and its suitability to serve as an explanatory factor in the changed agricultural commodities prices. Secondly, it performs a retrospective analysis while considering many variables from three different groups: market fundamentals, financial and climatic. Thirdly, it uses relative importance analysis to reduce the number of features and include only those most essential for an accurate agricultural commoditie

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recordType

award

status

SIGNED

region

EU

value

183600.96

unit

EUR