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Theses

Agricultural Commodity Price Forecasting Using Comprehensive Machine-Learning Techniques

Abstract : Would it be possible to develop a forecasting tool for agricultural commodity (AC) prices that is both accurate and interpretable and publicly accessible? Such a tool could turn the forecasting and analysis of food prices into an implementable instrument used by whoever is concerned by food security. This PhD explores the feasibility of this idea in three parts: The first part aims to test the ability of several statistical and machine learning (ML) models to simulate changes in maize prices based on annual changes in maize production and yield observed in major producing regions. The second part of the thesis applies the models developed in the first part and adapt them to produce monthly forecasts of maize prices. We compare the performance of these models to that of forecasting techniques often used for time series analysis. Finally, the third part extends the model to consider two other different crops – soybeans and cocoa. We evaluate the forecasting ability of the techniques developed in the previous stages to predict price changes for soybeans and cocoa. Additionally, we test the sensitivity of the results relative to three geographic scales. Also is the application of ML methods to identify which production shocks drive price shocks. Overall, this thesis shows that ML methods are a potential tool for understanding and forecasting the impact of agricultural production on price variations. These approaches can be easily implemented since they rely on publicly available data, accessible via public website. These tools can thus contribute to democratising the analysis and forecasting of variation in AC prices.
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  • HAL Id : tel-03617779, version 1

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Rotem Zelingher. Agricultural Commodity Price Forecasting Using Comprehensive Machine-Learning Techniques. Economics and Finance. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASB065⟩. ⟨tel-03617779⟩

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