Agricultural Market Price Prediction Using Machine Learning and ARIMA Time Series Models: A Review

• Author(s): Vedant Joshi; Aadesh Polekar; Yash Pardeshi; Pranay Pawar; Prof. Tushar Kolhe
• Paper ID: 1711906
• Page: 1047-1054
• Published Date: 14-11-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 5 November-2025

Abstract

Agricultural commodity price forecasting is essential for farmers, traders, policy makers and supply-chain managers. Traditional econometric/time-series methods such as ARIMA/SARIMA have been widely used because of simplicity and interpretability, while machine learning (ML) and deep learning (DL) approaches (Random Forest, XGBoost, SVR, LSTM, CNN, hybrid ARIMA?LSTM) are increasingly applied to capture nonlinearities and complex patterns. This review synthesizes recent literature (2018?2025), compares ARIMA and ML approaches, highlights hybrid strategies, discusses datasets and evaluation practices, identifies common challenges (data quality, exogenous factors, explainability), and proposes promising research directions for robust, deployable forecasting systems.

Keywords

Agricultural price forecasting, time series analysis, ARIMA, SARIMA, machine learning, deep learning, LSTM, XGBoost, hybrid models, nonlinear prediction, data quality, feature engineering, explainable AI, agricultural economics, predictive analytics.

Citations

IRE Journals:
Vedant Joshi, Aadesh Polekar, Yash Pardeshi, Pranay Pawar, Prof. Tushar Kolhe "Agricultural Market Price Prediction Using Machine Learning and ARIMA Time Series Models: A Review" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 1047-1054 https://doi.org/10.64388/IREV9I5-1711906

IEEE:
Vedant Joshi, Aadesh Polekar, Yash Pardeshi, Pranay Pawar, Prof. Tushar Kolhe "Agricultural Market Price Prediction Using Machine Learning and ARIMA Time Series Models: A Review" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1711906