International Journal of Statistical Distributions and Applications

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Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model

Received: 23 June 2019    Accepted: 17 July 2019    Published: 05 August 2019
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Abstract

Price forecasting is more sensitive with vegetable crops due to their high nature of perishability and seasonality and is often used to make better-informed decisions and to manage price risk. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast price of tomatoes using monthly data for the period 1981 to 2013 obtained from the Ministry of Agriculture, Livestock and Fisheries (MALF) in the agribusiness department. Forecasting tomato prices was done using time series monthly average prices from January 2003 to December 2016. SARIMA (2, 1, 1) (1, 0, 1)12 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. The time series data of Tomatoes for wholesale markets in Nairobi are considered as the national average. The predictive ability tests RMSE = 32.063, MAPE = 125.251 and MAE = 22.3 showed that the model was appropriate for forecasting the price of tomatoes in Nairobi County, Kenya.

DOI 10.11648/j.ijsd.20190503.11
Published in International Journal of Statistical Distributions and Applications (Volume 5, Issue 3, September 2019)
Page(s) 46-53
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Tomatoes, SARIMA, Autocorrelation Function, Akaike Information Criterion, Jarque-Bera Test

References
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[13] Geoffrey, S. K., Hillary, N. K., Kibe, M. A., Mariam, M., & Mary, M. C. (2014). Challenges and strategies to improve tomato competitiveness along the tomato value chain in Kenya. International Journal of Business and Management, 9 (9), 205.
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[17] Sampson, W., Suleman, N., & Gifty, A. Y. (2013). Proposed seasonal autoregressive integrated moving average model for forecasting rainfall pattern in the Navrongo Municipality, Ghana. Journal of Environment and Earth Science, 3 (12), 80-85.
[18] Ivanišević, D., Mutavdžić, B., Novković, N., & Vukelić, N. (2015). Analysis and prediction of tomato price in Serbia. Economics of Agriculture, 62 (4), 951-962.
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Author Information
  • School of Pure and Applied Sciences, Kirinyaga University, Kirugoya, Kenya

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    Robert Mathenge Mutwiri. (2019). Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model. International Journal of Statistical Distributions and Applications, 5(3), 46-53. https://doi.org/10.11648/j.ijsd.20190503.11

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    Robert Mathenge Mutwiri. Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model. Int. J. Stat. Distrib. Appl. 2019, 5(3), 46-53. doi: 10.11648/j.ijsd.20190503.11

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    Robert Mathenge Mutwiri. Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model. Int J Stat Distrib Appl. 2019;5(3):46-53. doi: 10.11648/j.ijsd.20190503.11

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  • @article{10.11648/j.ijsd.20190503.11,
      author = {Robert Mathenge Mutwiri},
      title = {Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model},
      journal = {International Journal of Statistical Distributions and Applications},
      volume = {5},
      number = {3},
      pages = {46-53},
      doi = {10.11648/j.ijsd.20190503.11},
      url = {https://doi.org/10.11648/j.ijsd.20190503.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijsd.20190503.11},
      abstract = {Price forecasting is more sensitive with vegetable crops due to their high nature of perishability and seasonality and is often used to make better-informed decisions and to manage price risk. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast price of tomatoes using monthly data for the period 1981 to 2013 obtained from the Ministry of Agriculture, Livestock and Fisheries (MALF) in the agribusiness department. Forecasting tomato prices was done using time series monthly average prices from January 2003 to December 2016. SARIMA (2, 1, 1) (1, 0, 1)12 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. The time series data of Tomatoes for wholesale markets in Nairobi are considered as the national average. The predictive ability tests RMSE = 32.063, MAPE = 125.251 and MAE = 22.3 showed that the model was appropriate for forecasting the price of tomatoes in Nairobi County, Kenya.},
     year = {2019}
    }
    

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    AU  - Robert Mathenge Mutwiri
    Y1  - 2019/08/05
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    DO  - 10.11648/j.ijsd.20190503.11
    T2  - International Journal of Statistical Distributions and Applications
    JF  - International Journal of Statistical Distributions and Applications
    JO  - International Journal of Statistical Distributions and Applications
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    AB  - Price forecasting is more sensitive with vegetable crops due to their high nature of perishability and seasonality and is often used to make better-informed decisions and to manage price risk. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast price of tomatoes using monthly data for the period 1981 to 2013 obtained from the Ministry of Agriculture, Livestock and Fisheries (MALF) in the agribusiness department. Forecasting tomato prices was done using time series monthly average prices from January 2003 to December 2016. SARIMA (2, 1, 1) (1, 0, 1)12 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. The time series data of Tomatoes for wholesale markets in Nairobi are considered as the national average. The predictive ability tests RMSE = 32.063, MAPE = 125.251 and MAE = 22.3 showed that the model was appropriate for forecasting the price of tomatoes in Nairobi County, Kenya.
    VL  - 5
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