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

Robert Mathenge Mutwiri

doi:doi:10.11648/j.ijsd.20190503.11

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

Robert Mathenge Mutwiri

Published in International Journal of Statistical Distributions and Applications (Volume 5, Issue 3)

Received: 23 June 2019 Accepted: 17 July 2019 Published: 5 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.

Published in	International Journal of Statistical Distributions and Applications (Volume 5, Issue 3)
DOI	10.11648/j.ijsd.20190503.11
Page(s)	46-53
Creative Commons	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), 2019. Published by Science Publishing Group

Keywords

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

References

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APA Style

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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ACS Style

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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AMA Style

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://article.sciencepublishinggroup.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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TY  - JOUR
T1  - Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model
AU  - Robert Mathenge Mutwiri
Y1  - 2019/08/05
PY  - 2019
N1  - https://doi.org/10.11648/j.ijsd.20190503.11
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
SP  - 46
EP  - 53
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20190503.11
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
IS  - 3
ER  -

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Author Information

Robert Mathenge Mutwiri

School of Pure and Applied Sciences, Kirinyaga University, Kirugoya, Kenya

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Plain Text BibTeX RIS

APA Style

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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ACS Style

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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AMA Style

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://article.sciencepublishinggroup.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}
}

Copy | Download

TY  - JOUR
T1  - Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model
AU  - Robert Mathenge Mutwiri
Y1  - 2019/08/05
PY  - 2019
N1  - https://doi.org/10.11648/j.ijsd.20190503.11
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
SP  - 46
EP  - 53
PB  - Science Publishing Group
SN  - 2472-3509
UR  - https://doi.org/10.11648/j.ijsd.20190503.11
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
IS  - 3
ER  -

Copy | Download