Comparative Performance Analysis of ARIMA and Time Delay Neural Networks for Cauliflower Price Forecasting in North India: 23529
Price: ₹ 1000
https://doi.org/10.35716/IJED-23529
Author: Divyanshu, Chandresh Guleria, Pardeep Singh, Subhash Sharma, Rohit Kumar Vashishat and Priyank Sharma
Author Address: Department of Social Sciences, Dr. YSP UHF Nauni, Solan-173230 (H.P), Department of Social Sciences, Dr. YSP UHF Nauni, Solan (H.P)-173230
Abstract
Accurate
price forecasting of perishable crops is crucial for farmers' income stability.
This study compared ARIMA and TDNN models across selected markets. The results
indicated that ARIMA performed best in Ludhiana with the {(0,1,1) (0,1,0)}
model, yielding the lowest RMSE (103.22). However, TDNN outperformed ARIMA in
most cases, with Ludhiana again showing the best performance (RMSE 161.77)
using the (2:5s:1l) neural network model. These findings highlighted TDNN’s
superiority in capturing non-stationary price patterns. Policymakers should
integrate TDNN-based forecasting into agricultural price intelligence systems
to improve market predictability. Reliable price forecasts can aid farmers in
better planning, reducing distress sales, and enhancing profitability.
Additionally, government initiatives should promote digital tools and machine
learning models for real-time price predictions. Strengthening price
forecasting mechanisms will help mitigate market volatility, support
procurement decisions, and enhance overall agricultural sustainability in
India.
Keywords:
Box
Jenkins methodology, forecasting, neural networks, root mean square error, time
series.
JEL
Codes: C10,
C52, C53, Q11.
Description
Indian Journal of Economics and Development
Volume 21 No. 1, March 2025, 000-000
https://doi.org/10.35716/IJED-23529
Impact Factor: 0.3 (Web of Science)
NAAS Score: 6.30 (2025)
Indexed in Scopus (SJR = 0.13)
Resurchify Impact Score: 0.23
UGC Approved (UGC Care List Group II)
Index Copernicus (ICV 2023: 105.09)