# Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning

arXiv: https://arxiv.org/abs/2111.07508 · HTML: https://mlsanity.com/paper/2111.07508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 59 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Machine Learning Methods Economists Should Know About](https://mlsanity.com/paper/1903.10075/) (2019)
- [Forecasting Economics and Financial Time Series: ARIMA vs. LSTM](https://mlsanity.com/paper/1803.06386/) (2018)
- [Boosting insights in insurance tariff plans with tree-based machine learning methods](https://mlsanity.com/paper/1904.10890/) (2019)
- [Application of Machine Learning in Forecasting International Trade Trends](https://mlsanity.com/paper/1910.03112/) (2019)
- [Context-Driven Data Mining through Bias Removal and Data Incompleteness Mitigation](https://mlsanity.com/paper/1910.08670/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Review of Cybersecurity Incidents in the Food and Agriculture Sector](https://mlsanity.com/paper/2403.08036/) (2024)

## Abstract (arXiv)

International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers.