A COMPARATIVE STUDY OF FORECASTING AGRICULTURAL TIME SERIES: SOME SELECTED FOOD GRAIN IN BANGLADESH

Journal > Journal > Journal Vol – 10 No -2, January 2016 > A COMPARATIVE STUDY OF FORECASTING AGRICULTURAL TIME SERIES: SOME SELECTED FOOD GRAIN IN BANGLADESH

February 13, 2016

Journal Vol – 10 No -2, January 2016

Authors:

Md. Salauddin Khan,Masudul Islam,Md. Rasel Kabir,Lasker Ershad Ali,

DOI NO:

https://doi.org/10.26782/jmcms.2016.01.00002

Keywords:

ARIMA,ARMA,Forecast,Foodgrain,

Abstract

Bangladesh bureau of statistics (BBS) publish a statistical year book in every year where comprehensive and systematic summary of basic statistical information of Bangladesh covering wide range of fields. BBS also forecast different sectors such aseconomics, weather, agriculture etc in different time in this country. In this paper we mainly concern on the wheat, rice and maize food grain which plays a vital role in economic development of Bangladesh. The main purposes of this paper as to compare which techniques are better BBS’s or statistical techniques for forecasting. There are different forecasting models are available in statistics among these we used Auto regressive (AR), Moving Average(MA), Auto regressive Moving Average (ARMA)and Auto regressive Integrated Moving Average (ARIMA) models. For this reason, we clarify the stationary and non-stationary series by graphical method. On the basis of that,the stationary model is being set up asthe forecasting purpose. After analyze, we compare the forecasting result of our selective foodgrain and find that for ecasted valu esusing statistical techniques are nearest to the actual values compare to BBS’s project edvalues.

Refference:

I.  Abdullah, L. (2012),ARIMA Model for Gold Bullion Coin Selling PricesForecasting,International Journal of Advances in Applied Sciences. Vol. 1, No.4, pp. 153-158.

II. Anderson, T.W. (1984),An Introduction to Multivariate Statistical Analysis, 2nded.New York:John Wiley and Sons Inc.

III. Arumugan, P. and Anithakumari, V. (2013),Fuzzy Time SeriesMethod forForecastingTaiwanExport Data,International Journal of Engineering trendsandTechnology. Vol.8,pp. 3342-3347.

IV. Box, G. E. P. and Jenkins, G. M. (1976),Time Series Analysis: Forecasting andControl,SanFrancisco: Holden-Day.

V. Brokwell, P.J. and Davis, R.A. (1997),Introduction to Time Series andForecasting, Springer,New York

VI.Clements, M. and Hendry, D. (1998),Forecasting Economic Time Series,United UniversityPress, Cambridge.

VII. Deepak, P., et al. (2015), A Comparison of forecasting methods: Fundamentals,Polling, Prediction Markets, and Experts,A Journal of Prediction Markets,Vol.23, No.2, pp.1-31.

VIII. Diebold, F. (2004),Elements of Forecasting, 3rded. Thomsos sourth-westrn,India.

IX. Ediger, S.A, (2006),ARIMAForecasting of Primary Energy Demand by Fuel inTurkey,Energypolicy, Vol. 35, pp.1-8.

X. Gouriroux, C. and Monfort, A. (1997),Time Series and Dynammic Models,Giampiero,M.Gallo Cambridge.

XI. Gujarati, D.N. (2004),Basic Econometrics, 4thed.,McGraw Hill, New York.

XII. Hannan, E.J. (1994),Multiple Time Series,New York: John Wiley & Sons Inc.

XIII. Kumar, et al. (2009),Surface flux modelingusing ARIMA technique in humansubtropicalmonsoon area,Journal of Atmospheric and Solar-TerrestrialPhysics. Vol. 71, pp. 1293-1298.

XIV. Lloret, et al. (2000),Time Series Modeling of Landings in NorthMediterranean Sea,ICESJournal of Marine Science: Journal du Conseil. Vol.57, pp. 171-184.

XV. Mitrea, C. A., Lee, C. K.M. and Wu,Z. (2009), A Comparison betweenNeural Networks and Traditional Forecasting Methods: A Case Study,International Journal of Engineering Business Management, Vol. 1, No. 2, pp.19-24.

XVI. Mucuk, M. and Uysal, D. (2009),Turkey’s Energy Demand,Current ResearchJournal of SocialSciences, Vol.1(3), pp. 123-128.

XVII.Pingfan, H. and Zhibo, T. (2014), A comparison study of the forecasingperformance of three international organizations,JEL codes: C30, C80.

XVIII. Prindyck,R.S. and Rubinfeld, D.L. (1981),Economic Models and EconomicForecasts,3rded.McGraw-Hill, Inc.

XIX. Slvanathan, E. A. (1991),A Note on the Accuracy of Business Economists GoldPrice Forecasts,Australian Journal of Management. Vol. 16, pp. 91-94.

XX. Tseng, et al. (2001),Fuzzy ARIMA model for forecasting the foreign exchangemarket,FuzzySets and Systems. Vol. 118, pp. 1-11.

XXI. Wood, et al. (1996), Classifying Trend Movements in the MSCI U.S.A.Capitalmarket Index-A,Comparison of Regressions, ARIMA and Neural Network Method.Computers &Operation Research. Vol. 23, pp. 611-622.

Author(s): Md. Salauddin Khan, Masudul Islam, Md. Rasel Kabir and Lasker Ershad Ali View Download