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