Agriculture Commodity Prices Forecasting Using a Fuzzy Inference System

George S. Atsalakis ()
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George S. Atsalakis: Technical University of Crete

Chapter Chapter 19 in Agricultural Cooperative Management and Policy, 2014, pp 353-368 from Springer

Abstract: Abstract The objective of this chapter is to present a forecasting model of agricultural commodity prices using a Fuzzy Inference System. Recent studies have addressed the problem of commodity prices forecasting using different methods including artificial neural network and conventional model-based approaches. In this chapter, we proposed the use of a hybrid intelligent system called the Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast agri-commodity prices. In ANFIS, both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic are combined in order to provide enhanced forecasting capabilities compared to using a single methodology alone. Point accuracy of four agri-commodity prices (wheat, sugar, coffee, and cocoa) is appraised by computing root-mean-squared forecast errors and other well-known error measures. In terms of forecasting performance, it is clear from the empirical evidence that the ANFIS model outperforms over a feedforward neural network and two other conventional models (AR and ARMA).

Keywords: Root Mean Square Error; Hide Layer; Feedforward Neural Network; Mean Absolute Percentage Error; Adaptive Neuro Fuzzy Inference System (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:comchp:978-3-319-06635-6_19

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DOI: 10.1007/978-3-319-06635-6_19

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