Autoregressive Integrated Moving Average (ARIMA) Model with Genetic Algorithm to Forecast the Chilli and Turmeric Productions in India

Elakkiya N *

Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, India.

Banjul Bhattacharyya

Department of Agricultural Statistics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, India.

Sathees Kumar K

Department of Agricultural Economics, SRM College of Agricultural Sciences, SRMIST, Baburayanpettai, Chengalpattu, Tamil Nadu, India.

*Author to whom correspondence should be addressed.


Abstract

Aims: India holds the distinction of being the foremost producer of spices globally and has been long-run history in spice export. The quantity of Indian spice exports increased by 37% with $ 4.1 billion worth in 2021. With that, dried chilli, cumin, and turmeric alone contributed 44% of export value ($ 1.8 billion). Forecasting the production of major spices are key for exports and plays an essential role in supporting and achieving the target of $10 billion in exports by 2027.

Data Source: The time series data of chilli and turmeric production data in India from 1970-2020 periods was collected from Indiastat.

Methodology: The present study sought to forecast the production of chilli and turmeric in India using the ARIMA model and their parameters are estimated by stochastic optimization techniques (genetic algorithm). The parameters are estimated by minimizing the Mean Absolute Percentage Error (MAPE). Finally, ARIMA and ARIMA_GA models were compared based on their predictive ability.

Results: The Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were 254.01,11.32 (chilli) and 185.73, 15.24 (turmeric) for testing set of ARIMA_GA model which is lower than the fitted ARIMA model.

Conclusion: This work has shown that ARIMA_GA (2,1,1) has been the best model to forecast the chilli and turmeric production in India. ARIMA_GA model will cope with parsimony and convergence of likelihood function to global optimum problems. Therefore ARIMA with GA will able to model the complexity and uncertainty of the data.

Keywords: Maximum likelihood estimate, ARIMA, genetic algorithm and MAPE


How to Cite

Elakkiya N, Bhattacharyya , B., & Sathees Kumar K. (2024). Autoregressive Integrated Moving Average (ARIMA) Model with Genetic Algorithm to Forecast the Chilli and Turmeric Productions in India. Journal of Scientific Research and Reports, 30(6), 127–135. https://doi.org/10.9734/jsrr/2024/v30i62027

Downloads

Download data is not yet available.

References

India Brand Equity of Foundation (IBEF). Indian Spices, Spices Manufacturers and Exporters in India – IBEF; 2023. Available:http://www.ibef.org/exports/spice-industry-indias.

Directorate General of Commercial Intelligence and Statistics (DGCI&S); 2020. Available:https://www.indianspices.com/sites/default/files/Major_item_wise_Export_2020.pdf.

Mohammad N, Islam MA, Rahman M, Mahboob MG. Forecasting of maize production in bangladesh using time series data: The Bangladesh Journal of Agricultural Economics, 2022; 43(2): 18-32.

Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day; 1970.

Hamjah MA. Forecasting major fruit crops productions in Bangladesh using Box-Jenkins ARIMA model: Journal of Economics and Sustainable Development. 2014;5(7):96-107.

Brockwell PJ, Davis RA. Introduction to time series and forecasting. Springer-Verlag: New York. 1996;43-75

Dash A, Mahapatra SK. Using ARIMA model for yield forecasting of important pulse crops of Odisha, India: Amazonian Journal of Plant Research. 2020; 4(3): 646-659. Available: 10.26545/ajpr. 2020;b00073x.

Biswas R. Bhattacharyya B. ARIMA modeling to forecast area and production of rice in West Bengal: Journal of Crop and Weed. 2013;9(2):26-31.

Rolf S, Pravez, J. Urfer W. Model identification and parameter estimation of ARMA models by means of evolutionary algorithms: Computational Intelligence for Financial Engineering. 1997; 23:237-243.

Holland J. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: The University of Michigan Press; 1975.

Parviz L, Kholghi M, Hoorfar A. A comparison of the efficiency of parameter estimation methods in the context of streamflow forecasting: Journal of Agricultural Science and Technology. 2010; 12:47-60.

Zaer SA, Alsmadi MK, Alsmadi AM. ARMA model order and parameter estimation using genetic algorithms: Mathematical and Computer Modelling of Dynamical Systems: Methods, Tools and Applications in Engineering and Related Sciences. 2012;18(2):201-221.

Abbasi A, Khalili K, Behmanesh J, Shirzad A. Estimation of ARIMA model parameters for drought prediction using the genetic algorithm: Arabian Journal of Geosciences. 2021;14(10): 841.

Rathod S, Singh KN, Arya P, Ray M, Mukherjee A, Sinha K, Kumar P, Shekhawat RS. Forecasting maize yield using ARIMA-genetic algorithm approach: Outlook on Agriculture. 2017;46(4):265-271.

Alquraish M, Abuhasel K, Alqahtani S, Khadr M. SPI-based hybrid hidden Markov-GA, ARIMA-GA, and ARIMA-GA-ANN models for meteorological drought forecasting: Sustainability. 2021; 13.

Gunasekaran V, Kovi KK, Arja S, Chimata R. Solar irradiation forecasting using genetic algorithms: ArXiv preprint arXiv:2106.13956:2021 DOI:10.48550/arXiv.2106.13956.

Padmanaban K, Sahu PK, Narsimhaiah L. Production performance of chilli in India- A statistical approach: Advances in Life Sciences. 2016;5(10):4191-4200.

Dheer P. (2019). Time series modelling for forecasting of food grain production and productivity of India: Journal of Pharmacognosy and Phytochemistry. 2019;8(3):476-482.