Time Series Outlier Analysis of Tea Price Data
American Journal of Theoretical and Applied Statistics
Volume 2, Issue 1, January 2013, Pages: 1-6
Received: Dec. 21, 2012; Published: Jan. 10, 2013
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S. D. Krishnarani, Department of Statistics, Farook College, Kozhikode, Kerala, India
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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.
Autoregressive Integrated Moving Average; Additive Outlier; Innovational Outlier; Tea Price Data
To cite this article
S. D. Krishnarani, Time Series Outlier Analysis of Tea Price Data, American Journal of Theoretical and Applied Statistics. Vol. 2, No. 1, 2013, pp. 1-6. doi: 10.11648/j.ajtas.20130201.11
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