| Peer-Reviewed

Time Series Outlier Analysis of Tea Price Data

Published: 10 January 2013
Views:       Downloads:
Abstract

In this article Autoregressive Integrated Moving Average (ARIMA) models were fitted and outliers are identified for the auction price of tea in three regions- North India, South India and All India. The ARIMA models with seasonal differencing are found to be quite appropriate for the data. The region specific dynamics are distinctly assessed based on the autocorrelation functions. Further we are concerned with outliers in time series with two special cases, additive outlier (AO) and innovational outlier (IO).These outliers have been detected using two recent methods and conclusions drawn based on the data pertaining to the three regions. The reason for these types of outliers in the tea price have been further identified pointing towards the factors of environmental, weather conditions, pest attacks etc.

Published in American Journal of Theoretical and Applied Statistics (Volume 2, Issue 1)
DOI 10.11648/j.ajtas.20130201.11
Page(s) 1-6
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Autoregressive Integrated Moving Average; Additive Outlier; Innovational Outlier; Tea Price Data

References
[1] Box, G. E. P., Jenkins, G. M. and Reinsel, G. C., Time Series Analysis Forecasting and Control,3rd Edition, Pearson Education, Inc., 2009.
[2] Brockwell, P.J. and Davis, R. A., Introduction to Time Series and Forecasting,2nd Edition, Springer-Verlag, New York, Inc., 2006.
[3] Chatfield, C. , The Analysis of Time Series: An Introduction, 6th Edition, CRC Press LIC, 2009.
[4] Fox, A. J., Outliers in time series, J. Roy. Statist. Soc., B34, 350-363, 1972.
[5] Farley, E. V. , Jr. and Murphy, J. M. . Time series outlier analysis: Evidence for Management and Environmental In-fluences on Sockeye Salmon Catches in Alaska and Northern British Columbia, Alaska Fishery Research Bulletin, Vol.4, No.1, 1997.
[6] Louni, H. , Outlier detection in ARMA models, Journal of time series analy-sis., Vol.29,No.6,1057-1065, 2008.
[7] Kaya, A. , Statistical Modelling for Outlier Factors, Ozean Journal of Applied Sciences, 3 (no.1), 185-194. 2010.
[8] Ljung, G. M. and Box, G.E.P., On a measure of lack of fit in time series models, Biometrika, 65, 297-303, 1978.
[9] Saravanakumar, M. , An analysis of the tea price fluctuations in South India and North India, International Journal of Social Science Tomorrow, 1, no.7, 1-7, 2012.
Cite This Article
  • APA Style

    S. D. Krishnarani. (2013). Time Series Outlier Analysis of Tea Price Data. American Journal of Theoretical and Applied Statistics, 2(1), 1-6. https://doi.org/10.11648/j.ajtas.20130201.11

    Copy | Download

    ACS Style

    S. D. Krishnarani. Time Series Outlier Analysis of Tea Price Data. Am. J. Theor. Appl. Stat. 2013, 2(1), 1-6. doi: 10.11648/j.ajtas.20130201.11

    Copy | Download

    AMA Style

    S. D. Krishnarani. Time Series Outlier Analysis of Tea Price Data. Am J Theor Appl Stat. 2013;2(1):1-6. doi: 10.11648/j.ajtas.20130201.11

    Copy | Download

  • @article{10.11648/j.ajtas.20130201.11,
      author = {S. D. Krishnarani},
      title = {Time Series Outlier Analysis of Tea Price Data},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {2},
      number = {1},
      pages = {1-6},
      doi = {10.11648/j.ajtas.20130201.11},
      url = {https://doi.org/10.11648/j.ajtas.20130201.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20130201.11},
      abstract = {In this article Autoregressive Integrated Moving Average (ARIMA) models were fitted and outliers are identified for the auction price of tea in three regions- North India, South India and All India. The ARIMA models with seasonal differencing are found to be quite appropriate for the data. The region specific dynamics are distinctly assessed based on the autocorrelation functions. Further we are concerned with outliers in time series with two special cases, additive outlier (AO) and innovational outlier (IO).These outliers have been detected using two recent methods and conclusions drawn based on the data pertaining to the three regions. The reason for these types of outliers in the tea price have been further identified pointing towards the factors of environmental, weather conditions, pest attacks etc.},
     year = {2013}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Time Series Outlier Analysis of Tea Price Data
    AU  - S. D. Krishnarani
    Y1  - 2013/01/10
    PY  - 2013
    N1  - https://doi.org/10.11648/j.ajtas.20130201.11
    DO  - 10.11648/j.ajtas.20130201.11
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 1
    EP  - 6
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20130201.11
    AB  - In this article Autoregressive Integrated Moving Average (ARIMA) models were fitted and outliers are identified for the auction price of tea in three regions- North India, South India and All India. The ARIMA models with seasonal differencing are found to be quite appropriate for the data. The region specific dynamics are distinctly assessed based on the autocorrelation functions. Further we are concerned with outliers in time series with two special cases, additive outlier (AO) and innovational outlier (IO).These outliers have been detected using two recent methods and conclusions drawn based on the data pertaining to the three regions. The reason for these types of outliers in the tea price have been further identified pointing towards the factors of environmental, weather conditions, pest attacks etc.
    VL  - 2
    IS  - 1
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics, Farook College, Kozhikode, Kerala, India

  • Sections