Indian Journal of Science and Technology

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Application of Time Series Models


Affiliations
1 Department of Mathematics, Pachaiyappa’s College for Men, Kancheepuram–631 501, TN, India
 

Forecasting is an ultimate aim in the study of time series analysis. Anyone who is engaged in planning, controlling and managing projects, personnel, finance and operations will be interested in knowing what will happen in future with the analysis of the available data

Keywords

Time Series, ARMA, ARIMA, ARARMA, Fractional Differencing
User

  • Box GEP and Jenkins GM (1976) Time series analysis: forecasting and control, revised edn, San Francisco: Holden-day.

  • Hosking JRM (1981) Fractional differencing. Biometrika, 68(1), 165-176.

  • Montgomary DC and Johnson LA (1976) Forecasting and time series analysis. McGraw Hill Inc., San Francisco. pp: 231-232.

  • Parzen E (1982) ARARMA models for time series analysis and forecasting. J. Forecasting. 1, 67-82.

  • Sekar P and Sreenivasan M (1996) Simulation and modeling of time series using fractional differencing. Proc. of Int. Conf. on Stochastic Process. Dec. 26-29, Cochin, India, pp:225-233.


Abstract Views: 366

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  • Application of Time Series Models

Abstract Views: 366  |  PDF Views: 95

Authors

P. Sekar
Department of Mathematics, Pachaiyappa’s College for Men, Kancheepuram–631 501, TN, India

Abstract


Forecasting is an ultimate aim in the study of time series analysis. Anyone who is engaged in planning, controlling and managing projects, personnel, finance and operations will be interested in knowing what will happen in future with the analysis of the available data

Keywords


Time Series, ARMA, ARIMA, ARARMA, Fractional Differencing

References





DOI: https://doi.org/10.17485/ijst%2F2010%2Fv3i9%2F29883