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Then, the essential timing requirement of predictive solutions is not fulfilled.
On the other hand, a small number of basic predictive solutions are also available.
The algorithms are constantly searching for the best, most predictive solutions, while work is ongoing with the academic world to devise new techniques for bringing further scientific rigour to the models of measuring influence.
However, traditional reactive methodologies and basic proposed predictive solutions are not valid either due to the extremely dynamical behavior of CPS or because the high number of involved devices prevents fulfill the timing requirements.
In both of these applications, the approach is demonstrated to be able to successfully reconstruct functional corrections and yield accurate predictive solutions while providing a measure of model form uncertainties.
Moreover, the predictive solutions found by GP in a dataset turned out to be significantly predictive of survival also in another, independent dataset without any further training.
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The proposed basic predictive solution cannot be employed as they do not fulfill the timing requirements.
Therefore we can confirm that the proposed model is a powerful predictive solution for the stock market prices.
Moreover, reactive techniques which complement the proposed predictive solution are necessary, in order to create a really useful hardware management algorithm for CPS.
However, traditional reactive techniques (such as timers) may complement the proposed predictive solution, especially in commercial deployments where unpredicted system failures might occur and have to be managed.
Therefore, in this paper, we present an advance predictive solution for managing the mobility and device lifecycle, being able to meet all requirements of CPS.
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