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IJSTR >> Volume 9 - Issue 2, February 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Univariate Time Series Models For Fuel Price

[Full Text]

 

AUTHOR(S)

M. Sulaiga Beevi , K. Senthamarai Kannan , S. Syed Ali Fathima

 

KEYWORDS

Fuzzy Time Series, Double Exponential Smoothing, ARIMA and Forecasting.

 

ABSTRACT

any of the researcher, economist and a businessman currently exists interested in estimating the future of population, prices, national income etc. Accuracy of the future forecasts depends to a large extent on the success or failure. Hence the analysis of time series assumes just as great importance in the study of every single one economic problems.Here many methods are currently available in the literature to solve the problem of prediction. This study provides a detailed comparison of Fuzzy Time Series (FTS), Double Exponential Smoothing (DES) model and Auto Regressive Integrated Moving Average (ARIMA) model. Future values currently are forecasting using FTS, DES and ARIMA model. Forecasted values for Mean Square Error (MSE), Root Mean Square (RMSE), and Mean Absolute Percentage Error (MAPE) are calculated individually for all the three methods.

 

REFERENCES

[1] R. G. Brown, Statistical Forecasting for Inventory Control, New York: McGraw-Hill ,1959.
[2] L. Broze and G. Mélard. Exponential smoothing: Estimation by maximum likelihood, Journal of Forecasting, Vol 9, pp 445-455, 1990.
[3] C. Chatfield, and M. Yar. Prediction intervals for multiplicative Holt-Winters, International Journal of Forecasting, vol 7, pp 31-37, 1991.
[4] Song, Q., and B. S. Chissom ,Fuzzy time series and its models,Fuzzy Sets andSystems, 54(3), 269-277(1993).
[5] Song, Q., and B. S. Chissom , Forecasting enrollments with fuzzy time series- Part II,Fuzzy Sets and Systems, 62(l), 1-8(1994).
[6] Chen, S. M., Forecasting enrollments based on fuzzy time series,Fuzzy Sets and Systems, 81(3), 311 – 319(1996).
[7] T. M. J. A. Cooray, Applied Time Series Analysis and Forecasting, Narosa Publishing House, 2008.

[8] Wang, C. CA Comparison study between fuzzy time series model and ARIMA Model for Forecasting Taiwan Export, Expert Systems with Applications, 38, 9296–9304,2011.
[9] Yun-sheng Hsu et.al ,A Comparison of ARIMA fore casting and heuristic modeling, Applied Financial Economics, 21, 1095-1102,2011.
[10] Niyimbanira .F.,Fuel price and exchange rate dynamics in south Africa:A Time series Analysis ,Corporate Ownership and Control ,12(4)2015.
[11] Hansun S. A New Approach of Brown’s Double Exponential Smoothing Method in Time Series Analysis, Balkan Journal of Electrical &Computer Engineering,4(2)2016.
[12] Edward.A. and Manoj.J.,One Forecastmodel using ARIMA for stock price of Automobile sector International Journal of Research in Marketing.6(4)2016.
[13] Tularam, G.A. and Saeed, T. Oil-Price Forecasting Based on Various Univariate Time-Series Models. American Journal of Operations Research, 6, 226-235, 2016.
[14] Tsai M-C, Cheng C-H, Tsai M-I, Shiu H-Y, Forecastingleading industry stock prices based on a hybrid time-series forecast model. PLoS 13(12),2018.
[15] K. Senthamarai Kannan , M. Sulaiga Beevi , and S. Syed Ali Fathima ,Comparison of Fuzzy Time Series and ARIMA Model,INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 08, AUGUST 2019