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Comparison Study on Exponential Smoothing and ARIMA Model for the Fuel Price

  • Sheik Abdullah Abdul Azees
  • Ramraj Sasikumar
Chapter
Part of the Asset Analytics book series (ASAN)

Abstract

Stock market volatility is important for investment, option pricing and financial market regulation. In recent years, stock market analysis and prediction have the greatest significance for many professionals in the fields of finance and stock exchange. There are many methods available in the literature to solve the problem of future prediction. The present study provides a detailed comparison of Single Exponential Smoothing (SES) model and Autoregressive Integrated Moving Average (ARIMA) model. Future values are forecasting using SES and ARIMA model. Forecasted values for different îś values are calculated from SES method and ARIMA (0, 2, 3). Also, Mean Square Error (MSE), Mean Absolute Deviation (MAD), Root Mean Square (RMSE) and Mean Absolute Percentage Error (MAPE) are calculated individually for both methods.

Keywords

Time series Single exponential smoothing ARIMA and forecasting 

Notes

Acknowledgements

The authors acknowledge University Grants Commission (UGC) for providing the infrastructure facility under the scheme of SAP (DRS-I). The first author acknowledges UGC for financial support to carry out this research work under Basic Science Research Fellowship (BSRF) for meritorious students.

References

  1. 1.
    Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, Oakland CalifGoogle Scholar
  2. 2.
    Brown RG (1959) Statistical Forecasting for Inventory Control. McGraw-Hill, New YorkGoogle Scholar
  3. 3.
    Cooray TMJA (2008) Applied Time Series Analysis and Forecasting. Narosa Publishing House, New DelhiGoogle Scholar
  4. 4.
    Gardner ES (1985) Exponential Smoothing: the state of the art. J Forecast 4:1–28CrossRefGoogle Scholar
  5. 5.
    Hyndman RJ, Koehler AB, Snyder RD, Grose S (2002) A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast 18:439–454CrossRefGoogle Scholar
  6. 6.
    Kim JH (2003) Forecasting Autoregressive time series with bias corrected parameter estimators. Int J Forecast 19:493–502CrossRefGoogle Scholar
  7. 7.
    Landsman WR, Damodaran A (1989) A comparison of quarterly earnings per share forecasts using James-Stein and unconditional least squares parameter estimators. Int J Forecast 5(4):491–500CrossRefGoogle Scholar
  8. 8.
    Taylor JW (2003) Exponential smoothing with a damped multiplicative trend. Int J Forecast 19:715–725CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of StatisticsManonmaniam Sundaranar UniversityTirunelveliIndia

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