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...

Most of predictive maintenance technologies are inaccessible to small scale and medium scale industries due to their demanding cost. This paper proposes a predictive maintenance policy using failure mode effect and criticality analysis (FMECA) and non-homogeneous Poisson process (NHPP) models...

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 study tested the unbiased pricing hypothesis in the copper, aluminum, nickel, and lead markets for the period October 2011 to May 2021. Wavelets and a time-varying parameter model with Bayesian priors were the primary tools. Each metal market was found to be weak market efficient, the...

We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping...

This paper systematically studies the use of mixed-frequency data sets and suggests that the use of high frequency data in forecasting economic aggregates can improve forecast accuracy. The best way of using this information is to build a single model, for example, an ARMA model with missing...

This paper introduces a general class of combined neural network-GARCH models suitable to financial time series analysis. We put special emphasis on designing a full model-building cycle for this class of models that includes all stages of econometric modelling (specification, estimation and...

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic...

This paper systematically studies the use of mixed-frequency data sets and suggests that the use of high frequency data in forecasting economic aggregates can improve forecast accuracy. The best way of using this information is to build a single model, for example, an ARMA model with missing...