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

Abstract

This 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

REVIEWING EDITOR:

• Manuel Tejada, Universidad de Sevilla, Spain

SUBJECTS:

• Agriculture & Environmental Sciences
• Agriculture and Food
• Agriculture
• Econometrics
• Economic Forecasting

Acknowledgements

The authors have no acknowledgments to make.

Availability of supporting data

All data generated or analysed during this study are included in this article and its Supplementary Information files.

Author’s contributions

Anisha Giri: Conceived and designed the study, performed data collection and compilation from secondary sources, conducted analysis and conducted relevant literature review.

Vijay Raj Giri: Contributed to documenting the research process, prepared the manuscript, compiled reports and meticulously verified the analysis conducted by the main author.

Disclosure statement

The authors declare that they have no competing interests.

Additional information

Funding

The research reported in this article received no specific funding.

Notes on contributors

Anisha Giri

Anisha Giri is an Agrieconomist at the Government of Nepal, working under the Ministry of Agriculture and Livestock Development. She holds a Master’s degree in Agronomy from the Agriculture & Forestry University (AFU), Nepal, as well as a Master’s in Public Administration (MPA) and a Bachelor’s of Science (BSc) degree in Agriculture from Tribhuvan University (T U), Nepal. With seven years of dedicated service, Anisha is now seeking to explore and integrate machine learning and AI techniques in her study. Her other research interests include agribusiness management, consumer behaviour, econometrics, and statistical analysis.

Vijay Raj Giri

Vijay Raj Giri is a doctoral candidate in Mechanical Sciences and Engineering at the University of Michigan-Dearborn and a Graduate Student Research Assistant. He holds an MSc in Renewable Energy Engineering and a Bachelor’s in Mechanical Engineering from the Pulchowk Campus, Institute of Engineering, Tribhuvan University, along with an MPA from T U. His research focuses on combustion, SI engine modeling, knocking phenomena, phenomenological models, and the application of machine learning in these areas.