Home » Sequential Pattern Detection and Time Series Models for Predicting IED Attacks by William B. Stafford
Sequential Pattern Detection and Time Series Models for Predicting IED Attacks William B. Stafford

Sequential Pattern Detection and Time Series Models for Predicting IED Attacks

William B. Stafford

Published
ISBN :
Kindle Edition
64 pages
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 About the Book 

Improvised explosive device (IED) attacks are a significant threat tocoalition forces. Defeating IEDs as weapons of strategic influence has becomea major objective of Combatant Commanders and their respective Joint TaskForces. This thesis attempts to identify new approaches that can helpoperational forces mitigate the risk of IED attacks by identifying commonsequences of events that occur before an IED attack and forecasting the number of attacks in the immediate future. Using the CARMA association rulesalgorithm on historical data of religious, political, and IED attack events, amodel is developed to explore commonly occurring sequences of events leading to an insurgency IED attack and to predict events that are likely to occur given the sequence observed to date. Time series models are also generated to identify trends and relationships that can be helpful in forecasting future monthly IED attacks based upon previous actual historical attacks. Theidentified sequences and forecasts could be used to help plan troop movements, rotations, force levels, as well as allocating limited resources to address imminent threats.