Network of global food prices represented by interconnected nodes with grains and oilseeds.

Decoding Market Trends: What the Grains and Oilseeds Index Reveals About Food Prices

Kai Mendoza in Business & Economy August 2025 • 4 min read.

"Uncover the hidden patterns in grain and oilseed prices using network analysis, revealing critical insights for consumers and investors alike."

Food prices are a cornerstone of global stability, impacting everything from national security to social order. When food costs surge, the repercussions can include inflation, famine, and widespread social unrest. This makes understanding and predicting food price fluctuations not just an economic concern but a critical imperative for maintaining global stability.

Enter the Grains and Oilseeds Index (GOI), a key barometer of global agricultural markets. This index, along with its five sub-indices covering wheat, maize, soybeans, rice, and barley, provides daily price indicators reflecting changes in the spot markets of staple agro-food crops. By analyzing these indices, we gain valuable insights into the complex dynamics influencing our food supply.

Traditional methods of analyzing time-series data often fall short when trying to capture nonlinear associations and long-range dependencies. That's where the transformative potential of mapping time series onto complex networks comes into play. Using a technique called visibility graph (VG) analysis, we can convert these indices into networks, allowing us to harness the power of network science to dissect the multifaceted complexity of temporal data and potentially predict future trends.

Visibility Graphs: A New Lens for Understanding Food Prices

Network of global food prices represented by interconnected nodes with grains and oilseeds.

The study "Visibility graph analysis of the grains and oilseeds indices," employs visibility graph (VG) analysis to transform the Grains and Oilseeds Index (GOI) and its sub-indices into complex networks. This approach reveals underlying patterns and dependencies in food price data that might be missed by traditional analytical methods. Here's a simplified breakdown of how this works and what it uncovers:

The core idea involves converting time series data—in this case, the GOI and its sub-indices—into a network representation. Imagine each data point in the time series as a node in a network. The connections (edges) between these nodes are determined by their "visibility," meaning whether two data points can "see" each other without being obstructed by intervening points. This transformation allows us to apply tools from network science to analyze the data.

Node Creation: Each data point in the time series becomes a node in the network.
Edge Connection: Two nodes are connected if a straight line can be drawn between their corresponding data points without being blocked by any intervening data points.
Network Analysis: Once the network is constructed, various metrics like degree distribution, clustering coefficient, and average path length are calculated to understand the network's structure.

This method captures nonlinear associations and long-range dependencies inherent in the time series, uncovering topological structures and dynamic characteristics that traditional statistical approaches may miss. The analysis provides insights into the stability and relationships within the market, offering a new perspective on how food prices fluctuate.

The Future of Food Price Analysis

The research highlights the potential of using network science to understand and predict commodity price dynamics. By identifying trends, structures, and properties within visibility graphs, new avenues open for understanding market behaviors. The study suggests further research is needed, including extended time series analysis, comparative studies with other financial instruments, and detailed investigations into the mechanisms driving network properties. Incorporating external factors such as geopolitical events, climate change impacts, and economic indicators could provide a more holistic view of commodity markets.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.physa.2024.130004,

Title: Visibility Graph Analysis Of The Grains And Oilseeds Indices

Subject: econ.gn physics.soc-ph q-fin.ec

Authors: Hao-Ran Liu, Ming-Xia Li, Wei-Xing Zhou

Published: 12-04-2023

Everything You Need To Know

What is the Grains and Oilseeds Index (GOI), and why is it important?

The Grains and Oilseeds Index (GOI) is a key indicator of global agricultural markets. It, along with its five sub-indices (wheat, maize, soybeans, rice, and barley), provides daily price indicators reflecting changes in the spot markets of staple agro-food crops. Its importance lies in its ability to offer insights into the complex dynamics influencing food supply, which in turn impacts global stability, inflation, and social order. Understanding the GOI allows consumers and investors to get critical insights into the food price fluctuations and make informed decisions.

How does visibility graph (VG) analysis work when applied to the Grains and Oilseeds Index (GOI)?

VG analysis transforms time series data, such as the GOI and its sub-indices, into complex networks. Each data point in the time series becomes a node in the network. Connections (edges) are established between nodes based on "visibility" – whether a straight line can be drawn between their corresponding data points without obstruction. Once the network is constructed, metrics like degree distribution, clustering coefficient, and average path length are calculated to understand the network's structure. This method captures nonlinear associations and long-range dependencies inherent in the time series, revealing topological structures and dynamic characteristics that traditional statistical approaches might miss.

What are the potential benefits of using network science, specifically visibility graph analysis, for analyzing the Grains and Oilseeds Index (GOI)?

Using network science, particularly VG analysis, to study the GOI offers several benefits. It allows for the identification of trends, structures, and properties within visibility graphs that may be missed by traditional methods. This approach unveils underlying patterns and dependencies in food price data, which can provide a fresh perspective on market behaviors and price trends. Furthermore, this method captures nonlinear associations and long-range dependencies in time series data, uncovering topological structures and dynamic characteristics to potentially predict future trends in food prices.

What are some external factors that could be incorporated to enhance the analysis of the Grains and Oilseeds Index (GOI) using network science?

To provide a more holistic view of commodity markets, external factors should be considered alongside the analysis of the GOI using network science. These factors include geopolitical events, climate change impacts, and various economic indicators. Geopolitical events can significantly disrupt supply chains and influence prices. Climate change impacts, such as droughts or floods, can affect crop yields. Economic indicators, such as inflation rates and currency exchange rates, also play a critical role in shaping market dynamics.

How could the insights gained from analyzing the Grains and Oilseeds Index (GOI) using visibility graph analysis be applied in practice?

The insights gained from analyzing the GOI using visibility graph analysis can be applied in various practical ways. Investors can use these insights to anticipate price movements and make informed decisions. Policymakers can use this information to understand and address potential food price instability and the related risks of inflation, famine, and social unrest. Consumers can use this data to understand market trends and make more informed choices. In essence, this analysis can help to predict trends, understand market behaviors, and ensure global food security by understanding the fluctuations of the GOI.