Chapter Writing: Time Series Analysis Stationarity

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¶ … Malicious network intrusion is typically associated with specific data trends and data alerts through which network attacks can be detected and mitigated. The evaluation of those data to illuminate identifiable trends relies on sequential observation at regular time intervals. This time-series-process approach to data analysis can apply either to single-series (i.e. univariate) observations or to multiple-series (i.e. multivariate) observations.

Stationery time series analysis is particularly useful in predictive modeling, but requires statistical uniformity of the observations (i.e. random variables) over time. Time series analysis is dependent on constant variance about a fixed mean. Moreover, that mean must be a constant and not a function of time shift, making it "weakly stationary."

Time series analyses that satisfy the applicable criteria allow IT security to detect and identify the nature and significance of non-randomness in data. Time series modeling exploits data trends in the past to formulate predictive models of future behavior. In principle, this is made possible by permitting the dependent variables to reflect past data and past independent-variable… [END OF PREVIEW]

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APA Format

Time Series Analysis Stationarity.  (2011, August 30).  Retrieved November 19, 2019, from

MLA Format

"Time Series Analysis Stationarity."  30 August 2011.  Web.  19 November 2019. <>.

Chicago Format

"Time Series Analysis Stationarity."  August 30, 2011.  Accessed November 19, 2019.