Forecasting cauliflower prices in Nepal: a comparative analysis using seasonal time series and nonlinear models

Abstract

AbstractThis study aims to examine the seasonal price trends of cauliflower in the Nepalese market over the past decade, considering its significance as a major vegetable in terms of production and land area. The primary goal is to predict short-term market prices using econometric time-series analysis and artificial neural networks (ANNs), providing valuable insights for stakeholders, such as farmers, policymakers, researchers and students to make informed decisions and implement effective strategies for production, marketing, and distribution. The data, derived from the annual reports of the Kalimati Fruits and Vegetable Market covering April 2013 to March 2023, serves as the foundation for the analysis. Utilising the Seasonal Auto-Regressive Integrated Moving Average (SARIMA), Long Short-Term Memory Recurrent Neural Network (LSTM RNN) and Facebook (Fb) Prophet models, the study probes into the intricate seasonal patterns and trends in cauliflower prices. In contrast to conventional literature trends, the results of this study highlight the superior forecast accuracy of the SARIMA model, sizing the need for tailored-modeling approaches to address the complexities of the agricultural commodity market. The findings reveal an overall stable price structure in Nepal, implying the necessity for strategic planning to address potential challenges for cauliflower growers. The study recommends off-season cultivation to manage supply-demand imbalances during peak periods, enabling farmers to optimise profits and promote sustainable agricultural practices using policy interventions.

Keywords

• Cauliflower prices
• SARIMA
• time series analysis
• LSTM RNN
• Fb Prophet model
• forecasting