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This paper discusses ARIMA modeling as a technique for analyzing time series data. First, the paper introduces the topic and then proceeds to analyze the random shocks (or stochastic component) that lie at the core of ARIMA modeling. Finally, the paper addresses the intervention component--the actual effect of the intervention on the time series, focusing on the types of effects that occur in time series data.
From the Paper:"There are a number of ways to deal with the problem of autocorrelation. One of the easier and more practical ways is to control for it by modeling it. Autocorrelation is one form of noise that, if not controlled, will confound the analysis. Noise may also result from trend (tendency for the series to drift up or down), seasonality (systematic cyclical 'spikes'), or random error (around a mean level) (McDowall, McCleary, Meidinger, & Hay, 1980). By modeling these potential sources of variance, ARIMA modeling accounts for all sources of noise, thereby enabling a rigorous analysis of the effect of the intervention. The general ARIMA model is delineated as Yt = Nt + It, where Yt is the time series, Nt is noise, and It is the intervention."
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Cite this Research Paper:
ARIMA Modeling (2010, September 17) Retrieved September 19, 2021, from https://www.academon.com/research-paper/arima-modeling-129205/
"ARIMA Modeling" 17 September 2010. Web. 19 September. 2021. <https://www.academon.com/research-paper/arima-modeling-129205/>