During the lifecycle of mega engineering projects such as: energy facilities,

infrastructure projects, or data centers, executives in charge should take into account

the potential opportunities and threats that could affect the execution of such projects.

These opportunities and threats can arise from different domains; including for

example: geopolitical, economic or financial, and can have an impact on different

entities, such as, countries, cities or companies. The goal of this research is to provide

a new approach to identify and predict opportunities and threats using large and diverse

data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to

inform domain specific foresights. In addition to predicting the opportunities and

threats, this research proposes new techniques to help decision-makers for deduction

and reasoning purposes. The proposed models and results provide structured output to

inform the executive decision-making process concerning large engineering projects

(LEPs). This research proposes new techniques that not only provide reliable timeseries

predictions but uncertainty quantification to help make more informed decisions.

The proposed ensemble framework consists of the following components: first,

processed domain knowledge is used to extract a set of entity-domain features; second,

structured learning based on Dynamic Time Warping (DTW), to learn similarity

between sequences and Hierarchical Clustering Analysis (HCA), is used to determine

which features are relevant for a given prediction problem; and finally, an automated

decision based on the input and structured learning from the DTW-HCA is used to

build a training data-set which is fed into a deep LSTM neural network for time-series

predictions. A set of deeper ensemble programs are proposed such as Monte Carlo

Simulations and Time Label Assignment to offer a controlled setting for assessing the

impact of external shocks and a temporal alert system, respectively. The developed

model can be used to inform decision makers about the set of opportunities and threats

that their entities and assets face as a result of being engaged in an LEP accounting for

epistemic uncertainty.