Subject: food , food prices , food prices forecasting , Machine Learning , explainability
Year: 2023
Type: Proceedings
Title: Methodology for food prices forecasting
Author: Peshevski, Dimitar
Author: Todorovska, Ana
Author: Trajkovikj, Filip
Author: Hristov, Nikola
Author: Trajanoska, Milena
Author: Dobreva, Jovana
Author: Stojanov, Riste
Author: Trajanov, Dimitar
Abstract: Fluctuations in food prices play a pivotal role in maintaining economic equilibrium and influencing the very fabric of our everyday lives. This paper presents a comprehensive framework for modeling and analyzing food price trends in 12 select European countries, spanning from January 2013 to January 2023, utilizing advanced state-of-the-art Machine Learning techniques. To achieve this objective, historical price data and technical indicators are incorporated into the proposed XGBoost model alongside a baseline model. The model results are assessed using various measures, and a benchmark is established. Notably, the average achieved R2 for predicting food prices within the time frame from January 2020 to January 2022 is 0.85 and 0.64 from January 2021 to January 2023. The findings reveal the efficacy of the proposed model, providing valuable insights into food price forecasting model interpretability and laying the groundwork for further research, including exploration into areas such as food fraud, food sustainability, and other pertinent topics in food economics.
Publisher: IEEE
Relation: 2023 IEEE International Conference on Big Data
Identifier: oai:repository.ukim.mk:20.500.12188/29611
Identifier: http://hdl.handle.net/20.500.12188/29611
