Time Series Outlier Analysis of Tea Price Data

S. D. Krishnarani

doi:doi:10.11648/j.ajtas.20130201.11

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Time Series Outlier Analysis of Tea Price Data

S. D. Krishnarani

Published in American Journal of Theoretical and Applied Statistics (Volume 2, Issue 1)

Received: 21 December 2012 Accepted: Published: 10 January 2013

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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), 2024. 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.

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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

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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

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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

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@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}
}

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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  -

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Author Information

S. D. Krishnarani

Department of Statistics, Farook College, Kozhikode, Kerala, India

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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

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@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  -

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