This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning...

In this paper we have assessed an influence of the NYSE Stock Exchange indexes (DJIA and NASDAQ) and European Stock indexes (DAX and FTSE) on the Warsaw Stock Exchange index WIG within a framework of a GARCH model. By applying a procedure of checking predictive quality of econometric models as...

We study 30 vintages of FRB/US, the principal macro model used by the Federal Reserve Board staff for forecasting and policy analysis. To do this, we exploit archives of the model code, coefficients, baseline databases and stochastic shock sets stored after each FOMC meeting from the model’s...

Yearly weather variability has an impact on agricultural supply and farmer welfare. More accurate weather predictions may increase the net returns for farmers but the indirect environmental impact of forecasts has not been examined. A stochastic bioeconomic model, which includes weather...

This work builds upon the findings of the joint research undertaken by the Eurasian Development Bank (EDB) and the Eurasian Economic Commission (EEC) to create a system capable of generating economic forecasts for EAEU member states, subject to any applicable country-specific social components....

This paper proposes a predictive maintenance policy using modified failure mode effect and criticality analysis (Mod-FMECA) technique. FMECA is used to identify failure modes, reasons, effects and criticality of the system (machine/plant) but in Mod-FMECA in addition to the analysis carried for...

In this paper we address the issue of assessing and communicating the joint probabilities implied by density forecasts from multivariate time series models. We focus our attention in three areas. First, we investigate a new method of producing fan charts that better communicates the uncertainty...

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment....

This research examines the structural properties of the macroscopic model introduced in [AlShelahi and Saigal, 2018]. We present a theoretical analysis of the behavior of the macroscopic variables. In particular, we show that the model exhibits shock-like solutions, providing a new narrative for...