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Forecasting the red lentils commodity market price using SARIMA models

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Listed:
  • Roshani W. Divisekara

    (University of Peradeniya)

  • G. J. M. S. R. Jayasinghe

    (Uva Wellassa University)

  • K. W. S. N. Kumari

    (Uva Wellassa University)

Abstract

Canada is the world’s largest producer of lentils, accounting for 32.8% of total production in the world. However, the production of lentils are prone to fluctuate due to the impact of erratic factors such as weather conditions and economic crises. Consequently, the price of the commodity will be changed and volatile. Therefore, the approach of modeling and forecasting future price based on the preceding data will provide representative figures to make decisions regarding the lentil production for growers and end users. Hence, the objective of this study is to model and forecast the red lentil prices using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). Eight years of weekly data starting from 2010 to 2019 which comprise 521 observations, obtained from Saskatchewan.ca were used in this study. The average red lentil price in Saskatchewan was dollar 24.75 per 100 lb, and weekly prices were highly fluctuating over time. The seasonality and volatility of red lentils are modeled and forecasted by calculating the seasonal index and applying SARIMA models to the time series. The results reveal that the SARIMA (2,1,2)(0,1,1)[52] model provides the best in sample and out-sample performance when predicting the red lentil prices. Hence, this model can be utilized by both growers and end users in making optimal production decisions and in managing overall price risk.

Suggested Citation

  • Roshani W. Divisekara & G. J. M. S. R. Jayasinghe & K. W. S. N. Kumari, 2021. "Forecasting the red lentils commodity market price using SARIMA models," SN Business & Economics, Springer, vol. 1(1), pages 1-13, January.
  • Handle: RePEc:spr:snbeco:v:1:y:2021:i:1:d:10.1007_s43546-020-00020-x
    DOI: 10.1007/s43546-020-00020-x
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    References listed on IDEAS

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    1. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
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    Cited by:

    1. Anna Borucka, 2023. "Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth," Sustainability, MDPI, vol. 15(9), pages 1-21, April.

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