Food Security

, Volume 7, Issue 5, pp 1055–1070 | Cite as

Using time series structural characteristics to analyze grain prices in food insecure countries

Original Paper

Abstract

Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.

Keywords

Price analysis Characteristic based clustering Food security 

Notes

Acknowledgments

Kathryn Grace provided many insightful comments on an earlier version of this manuscript. This work was supported by US Geological Survey (USGS) cooperative agreement (#G09AC000001), the USGS Climate and Land Use Change program, NASA SERVIR, and NASA grants NNH12ZDA001N- IDS and NNX14AD30G. The Kenyan price data was provided courtesy of Blake Stabler at FEWS NET and the Kenyan Ministry of Agriculture.

Supplementary material

12571_2015_490_MOESM1_ESM.docx (480 kb)
ESM 1 (DOCX 480 kb)

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Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2015

Authors and Affiliations

  1. 1.Department of GeographyClimate Hazards Group, UC Santa BarbaraSanta BarbaraUSA
  2. 2.United States Geological Survey (U.S.G.S), and Department of GeographyUniversity of CA Santa BarbaraSanta BarbaraUSA

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