Abstract Extreme weather poses a major challenge to global food security by causing sharp drops in crop yield and supply. International crop trade can potentially alleviate such challenge by reallocating crop commodities. However, the influence of extreme weather stress and synchronous crop yield anomalies on trade linkages among countries remains unexplored. Here we use the international wheat trade network, develop two network-based covariates (i.e., difference in extreme weather stress and short-term synchrony of yield fluctuations between countries), and test specialized statistical and machine-learning methods. We find that countries with larger differences in extreme weather stress and synchronous yield variations tend to be trade partners and with higher trade volumes, even after controlling for factors conventionally implemented in international trade models (e.g., production level and trade agreement). These findings highlight the need to improve the current international trade network by considering the patterns of extreme weather stress and yield synchrony among countries. 

/pdf/wheat-trade-tends-to-happen-between-countries-with-3ta2rvnv.pdf

Wheat trade tends to happen between countries with contrasting extreme weather stress and synchronous yield variation

In this paper, we propose a general model for the soybean export market share dynamics and provide several theoretical analyses related to a special case of the general model. We implement machine and neural network algorithms to train, analyze, and predict US Gulf soybean market shares (target variable) to China using weekly time series data consisting of several features between January 6, 2012 and January 3, 2020. Overall, the results indicate that US Gulf soybean market shares to China are volatile and can be effectively explained (predicted) using a set of logical input variables. Some of the variables, including shipments due at US Gulf port in 10 days, cost of transporting soybean shipments via barge at Mid‐Mississippi, and soybean exports loaded at US Gulf port in the past 7 days, and binary variables have shown significant influence in predicting soybean market shares.

Machine learning and neural network based model predictions of soybean export shares from US Gulf to China

Abstract Background: This paper presents a machine learning approach to forecast Croatia’s international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia. Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE). Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms. Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows.

A Machine Learning Approach to Forecast International Trade: The Case of Croatia

In the dynamic global trade environment, accurately predicting trade values of diverse commodities is challenged by unpredictable economic and political changes. This study introduces the Meta-TFSTL framework, an innovative neural model that integrates Meta-Learning Enhanced Trade Forecasting with efficient multicommodity STL decomposition to adeptly navigate the complexities of forecasting. Our approach begins with STL decomposition to partition trade value sequences into seasonal, trend, and residual elements, identifying a potential 10-month economic cycle through the Ljung–Box test. The model employs a dual-channel spatiotemporal encoder for processing these components, ensuring a comprehensive grasp of temporal correlations. By constructing spatial and temporal graphs leveraging correlation matrices and graph embeddings and introducing fused attention and multitasking strategies at the decoding phase, Meta-TFSTL surpasses benchmark models in performance. Additionally, integrating meta-learning and fine-tuning techniques enhances shared knowledge across import and export trade predictions. Ultimately, our research significantly advances the precision and efficiency of trade forecasting in a volatile global economic scenario.

https://downloads.hindawi.com/journals/ijis/2024/6176898.pdf

Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition

Trade data across multiple databases experience unavailability across some countries/forestry products, inconsistency, and unreliability; and these qualities manifest as discrepancies in the data. Literature provides evidence of discrepancies and inconsistencies within international trade statistics, including documented cases in which they are present within agriculture sector trade. 

/pdf/a-forestry-trade-data-discrepancy-analysis-1l29o5d3.pdf

A Forestry Trade Data Discrepancy Analysis

Focusing on seven major agricultural commodities with a long history of trade, this study employs data-driven analytics to decipher patterns of trade, namely using supervised machine learning (ML), as well as neural networks. The supervised ML and neural network techniques are trained on data until 2010 and 2014, respectively. Results show the high relevance of ML models to forecasting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, neural network approaches provide better fits over the long term.

/pdf/international-agricultural-trade-forecasting-using-machine-255k4bx656.pdf

International agricultural trade forecasting using machine learning

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International agricultural trade forecasting using machine learning