Volume 5, Issue 3, September 2019, Page: 46-53
Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model
Robert Mathenge Mutwiri, School of Pure and Applied Sciences, Kirinyaga University, Kirugoya, Kenya
Received: Jun. 23, 2019;       Accepted: Jul. 17, 2019;       Published: Aug. 5, 2019
DOI: 10.11648/j.ijsd.20190503.11      View  25      Downloads  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.
Keywords
Tomatoes, SARIMA, Autocorrelation Function, Akaike Information Criterion, Jarque-Bera Test
To cite this article
Robert Mathenge Mutwiri, Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model, International Journal of Statistical Distributions and Applications. Vol. 5, No. 3, 2019, pp. 46-53. doi: 10.11648/j.ijsd.20190503.11
Copyright
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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