Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model
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.
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.
Dickey, D. A. and Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74: 427-431.
Bollerslev, T. (1986). Generalised Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31: 307-27.
Box, G. E. P., Jenkins G. M., Reinsel, G. C. and Ljung, G. M., 2015, Time Series Analysis: Forecasting and Control (5th Ed.), John Wiley & Sons, Inc., Hoboken, New Jersey.
Zakoian, J. M., 1994. Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, Vol. 18, pp. 931-955.
Glosten, L. R., Jagannathan, R. and Runkle, D., 1993. On the Relationship between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, Vol. XLVIII, No. 5, pp. 1779-1801.
Bollerslev, T. and J. M. Wooldridge (1992). Quasi-maximum likelihood estimation and inference in dynamic models with time-varying Covariances, Econometric Reviews, 11, 143-173.
Zhang, J. H., Kong, F. T., Wu, J. Z., Zhu, M. S., Xu, K., and Liu, J. J., 2014. Tomato prices time series prediction model based on wavelet neural network. In Applied Mechanics and Materials (Vol. 644, pp. 2636-2640).
Muhammad Aamir, S., 2017. Price Forecasting Model for Perishable Commodities: A Case of Tomatoes in Punjab, Pakistan.
Fatima, A., Abid, S. and Naheed, S., 2015. Trends in Wholesale Prices of onion and potato in major markets of Pakistan: A time series Analysis. Pakistan Journal of Agricultural Research, 28 (2).
Reddy, A. A., 2019. Price Forecasting of Tomatoes. International Journal of Vegetable Science, 25 (2), pp. 176-184.
Gathondu, E. K. (2014). Modelling of wholesale prices for selected vegetables using time series models in Kenya (Masters thesis, University of Nairobi).
Ruttoh J K, Bett E K, and Nyairo N (2018). Empirical analysis of structure and conduct of tomato marketing in Loitoktok, Kajiado County, Kenya. International Journal of Agricultural Extension and Rural Development ISSN 3254-5428 Vol. 6 (4), pp. 628-638, April, 2018.
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.
Assis, K., Amran, A., & Remali, Y. (2010). Forecasting cocoa bean prices using univariate time series models. Researchers World, 1 (1), 71.
Fenyves, V., Orban, I., Dajnoki, K., & Nabradi, A. (2010). Evaluation of different predicting methods in forecasting Hungarian, Italian, and Greek lamb prices. Food Economics–Acta Agricult Scand C, 7 (2-4), 192-196.
Dieng, A. (2008). Alternative forecasting techniques for vegetable prices in Senegal. Revue sénégalais de recherches agricoles et agroallementalress, 1 (3), 5-10.
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.
Ivanišević, D., Mutavdžić, B., Novković, N., & Vukelić, N. (2015). Analysis and prediction of tomato price in Serbia. Economics of Agriculture, 62 (4), 951-962.
Boateng, F. O., Amoah-Mensah, J., Anokye, M., Osei, L., & Dzebre, P. (2017). Modeling of tomato prices in Ashanti region, Ghana, using seasonal autoregressive integrated moving average model. Journal of Advances in Mathematics and Computer Science, 1-13.
Adanacioglu, H., & Yercan, M. (2012). An analysis of tomato prices at wholesale level in Turkey: an application of SARIMA model. Custos e@ gronegócio on line, 8 (4), 52-75.
Zhang, H., & Rudholm, N. (2013). Modeling and forecasting regional GDP in Sweden using autoregressive models. Dalama University.
Box, G. E. P. and Jenkins, G. M. (2016). Time Series Analysis: Forecasting and Control. Holden-Day, San Fransisco.
Bhardwaj, S. P., Paul, R. K., Singh, D. R., & Singh, K. N. (2014). An empirical investigation of ARIMA and GARCH models in Agricultural Price Forecasting. Economic Affairs, 59 (3), 415.
Cryer, J. D., & Chan, K. S. (2008). Time series regression models. Time series analysis: with applications in R, 249-276.