Malicious Network Intrusion Is Typically Chapter

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Time series are self-similar in that they reveal the same patterns at different scales. The available methods for time series modeling include autoregressive, moving average, and their hybrid: autoregressive moving average. In autoregressive analysis, the current data are presumed to be functions of previous data points. In moving-average analysis, correlation between the past and present is achieved by introducing past data into the current process. Meanwhile, the autoregressive-moving-average hybrid approach is intended to accommodate circumstances where neither autoregressive nor moving average is capable of precisely fitting complex auto-correlational data.

In time series predictive modeling, estimating the expected prediction error is a function of the difference between the observed data and the values predicted statistically by the past data, otherwise known as residual analysis.

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This process also provides the primary means of checking the models generated through times series analyses. This residual-analysis approach is flexible enough to employ time plots, histograms, and auto-correlation. Likewise, time-series analyses allow the forecasting of future events for which no available data yet exists......

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