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Forecasting the Sugarcane Production in Bangladesh by Arima Model

Md. Moyazzem Hossain
Department of Statistics, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh

Faruq Abdulla
Department of Statistics, Islamic University, Kushtia-7003, Bangladesh

Abstract/ Executive Summary:

Around 70% of the world’s sugar is produced from sugarcane. The production of sugarcane is fluctuated from year to year due to the fluctuation of area under sugarcane cultivation. According to FAO, the sugar requirement per capita/day is 29g and Bangladesh requires 1.0-1.2 million tonnes of sugar/year to meet the demand of domestic consumption. To meet the demand of domestic consumption of sugar, it is too much essential to estimate the production of sugar since sugar is produced mainly from sugarcane in Bangladesh which leads us to do this research. The main purpose of this research is to identify the Auto-Regressive Integrated Moving Average (ARIMA) model that could be used to forecast the production of sugarcane in Bangladesh. This study considered the published secondary data of yearly sugarcane production in Bangladesh over the period 1971 to 2013. The best selected Box-Jenkins ARIMA model for forecasting the sugarcane productions in Bangladesh is ARIMA (0,2,1). The comparison between the original series and forecasted series shows the same manner indicating fitted model are statistically well behaved to forecast sugarcane productions in Bangladesh i.e., the models forecast well during and beyond the estimation period to a satisfactory level.

Keywords: Sugarcane, ARIMA Model, Forecasting, Bangladesh

Location: In Bangladesh

Start Date: 00-00-1971

End Date: 00-00-2013

Program Area: Crop-Soil-Water Management

Commodity: Sugarcane

Objectives:

To identify the Auto-Regressive Integrated Moving Average (ARIMA) model that could be used to forecast the production of sugarcane in Bangladesh.

Methodology:

This study considered the published secondary data of yearly sugarcane production in Bangladesh which was collected over the period 1971 to 2013 from the Food and Agricultural Organization (FAO) website (http://faostat3.fao.org/). The influential work of Box-Jenkins shifted professional attention away from the stationary serially correlated deviations from the deterministic trend paradigm toward the ARIMA (p, d, q) paradigm. It is popular because it can handle any series, stationary or not with or without seasonal elements. The basic steps in the Box-Jenkins methodology consist of the following five steps: Preliminary Analysis: Create conditions such that the data at hand can be considered as the realization of a stationary stochastic process. Identification of a Tentative Model: Specify the orders p, d, q of the ARIMA model so that it is clear the number of parameters to estimate. Empirical autocorrelation functions play an extremely important role to recognize the model. Estimation of the Model: The next step is the estimation of the tentative ARIMA model identified in step-2. By the maximum likelihood method, we estimate the parameters of the model. Diagnostic Checking: Check if the model is a good one using tests on the parameters and residuals of the model. Forecasting: If the model passes the diagnostics step, then it can be used to interpret a phenomenon, forecast. Before performing growth analysis it is necessary to estimate the growth model that best fits the time series. There are many summary statistics available in the literature for evaluating the forecast errors of any model, time series or econometric. We often do not compute all the statistics mainly because one of them is the function of the other. Thus, here an attempt is made to identify the best models for potato production in Bangladesh using the following contemporary model selection criteria, such as RMSPE, MPFE and TIC.

Source of Information: J. Stat. Appl. Pro. 4, No. 2, 297-303 (2015)

Article Link (Online Journal): http://dx.doi.org/10.12785/jsap/040214

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

A time series model is used to identify the patterns in the past movement of a variable and uses that information to forecast the future values. This study tried to fit the best model to forecast the sugarcane production in Bangladesh with the help of the latest available model selection criteria such as AIC, BIC, etc. The best ARIMA model is selected by Box-Jenkins methodology for forecasting the sugarcane productions in Bangladesh and the selected model is ARIMA (0,2,1). The comparison between the original series and forecasted series shows the same manner, indicating fitted model are statistically well behaved to forecast sugarcane productions in Bangladesh i.e., the models forecast well during and beyond the estimation period which reached at a satisfactory level. Thus, this model can be used for policy purposes as far as forecasts the sugarcane production in Bangladesh.

Knowledge Management: Journal