Forecasting the red lentils commodity market price using SARIMA models

被引:0
|
作者
Roshani W. Divisekara
G. J. M. S. R. Jayasinghe
K. W. S. N. Kumari
机构
[1] University of Peradeniya,Postgraduate Institute of Science
[2] Uva Wellassa University,Department of Science and Technology, Faculty of Applied Sciences
来源
SN Business & Economics | / 1卷 / 1期
关键词
Forecasting; Lentils; Model; SARIMA; Seasonal index;
D O I
10.1007/s43546-020-00020-x
中图分类号
学科分类号
摘要
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.
引用
收藏
相关论文
共 50 条
  • [31] EVALUATION OF METHODS USED IN COMMODITY PRICE FORECASTING
    Working, Elmer J.
    JOURNAL OF FARM ECONOMICS, 1930, 12 (01): : 119 - 138
  • [32] Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price
    Parida, Nirjharinee
    Mishra, Debahuti
    Das, Kaberi
    Rout, Narendra Kumar
    EVOLUTIONARY INTELLIGENCE, 2021, 14 (02) : 529 - 544
  • [33] Development and performance evaluation of hybrid KELM models for forecasting of agro-commodity price
    Nirjharinee Parida
    Debahuti Mishra
    Kaberi Das
    Narendra Kumar Rout
    Evolutionary Intelligence, 2021, 14 : 529 - 544
  • [34] Electricity Price Forecasting for Norwegian Day-Ahead Market using Hybrid AI Models
    Vamathevan, Gajanthini
    Dynge, Marthe Fogstad
    Cali, Umit
    2022 18TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM, 2022,
  • [35] Spot electricity price forecasting in Indian electricity market using autoregressive-GARCH models
    Girish, G. P.
    ENERGY STRATEGY REVIEWS, 2016, 11-12 : 52 - 57
  • [36] A Short-Term Electricity Price Forecasting on the Russian Market Using the SCARX Models Class
    Afanasyev, D. O.
    Fedorova, E. A.
    EKONOMIKA I MATEMATICESKIE METODY-ECONOMICS AND MATHEMATICAL METHODS, 2019, 55 (01): : 68 - 84
  • [37] Forecasting stock market volatility using commodity futures volatility information
    Liu, Guangqiang
    Guo, Xiaozhu
    RESOURCES POLICY, 2022, 75
  • [38] Price Forecasting in Energy Market
    Bilan, Yuriy
    Kozmenko, Serhiy
    Plastun, Alex
    ENERGIES, 2022, 15 (24)
  • [39] Exploring Models of Electricity Price Forecasting: Case Study on A FCAS Market
    Kato, Kenshiro
    Iwabuchi, Koki
    Watari, Daichi
    Zhao, Dafang
    E-ENERGY '23 COMPANION-PROCEEDINGS OF THE 2023 THE 14TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS, 2023, : 115 - 119
  • [40] ACCURACY OF DIFFERENT PRICE FORECASTING MODELS FOR MAIZE IN NIMBAHERA MARKET OF RAJASTHAN
    Sharma, Hemant
    Burark, S. S.
    INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES, 2016, 12 : 95 - 101