A Comparative Study of Time Series, Machine Learning, and Deep Learning Models for Forecasting Global Price of Wheat

Abhishek Yadav ()
Additional contact information
Abhishek Yadav: Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham

SN Operations Research Forum, 2024, vol. 5, issue 4, 1-24

Abstract: Abstract Wheat is a crucial grain in the global food supply and a key staple for many countries. Accurate wheat price forecasting is vital due to the economic impact of price volatility and supply–demand shocks. This study evaluated the forecasting performance of various time series, machine learning, and deep learning models in predicting global wheat prices from January 1990 to February 2024, using data from the Federal Reserve Economic Data on wheat and other commodity prices, and macroeconomic variables. Models tested were linear regression, partial least squares regression, autoregressive integrated moving average (ARIMA) and its variants, convolutional neural network (CNN), and long short-term memory (LSTM) were employed. Forecasting accuracy was assessed using mean absolute error and root mean squared error. The impact of the 2008 recession and COVID-19 pandemic was also considered. Overall, no single model excelled but ARIMA with exogenous variables (ARIMAX), partial least squares regression, and linear regression models performed best in capturing the influence of external variables on wheat prices. Seasonal ARIMA (SARIMA) performed the best among the univariate time series models. SARIMA was used to extrapolate variables for 6 months post-February 2024, which were fed into ARIMAX, linear, and partial least squares models for out-of-sample forecasting. The findings suggest that integrated models that combine historical data with external variables are needed for more accurate wheat price forecasting.

Keywords: Wheat prices; ARIMA; LSTM; Forecasting; Economic events; Machine learning; Time series (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-024-00395-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:5:y:2024:i:4:d:10.1007_s43069-024-00395-9

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-024-00395-9

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().