Machine Learning Techniques and Bayesian Methods Using Big Data: Perspectives and Forecasts in Micro- and Macro-Economic Issues

Dear Colleagues,

This Special Issue focuses on the broad topic of “Big Data Analytics”. Data is at the heart of the economy, helping companies and organizations to improve business processes, make better business decisions and policy-relevant strategies, monitor customers and competitors, and perform accurate forecasts.

However, over the past two decades, two main important open issues—among the others—matter: (i) how to develop a high performance computational approach to efficiently analyze Big Data, and (ii) how to design an appropriate mining algorithm to evaluate the usefulness of Big Data.

These days, Big Data are increasingly large and complex, combining structured, unstructured, and semi-structured data that traditional discriminant analyses are not always able to process, store, and manage them effectively and/or efficiently.

Machine Learning (ML) techniques represent advanced forms of analytics to extract more value from the data in their systems, providing cognitive capabilities across high dimensional sets of data. More precisely, they use data-driven algorithms and statistical-econometric models to evaluate datasets, to make inferences from identified settings, and then to perform better forecasts. 

In a world with ever-increasing amounts of data and analytical tools, Bayesian inference algorithms and Markov Chain Monte Carlo (MCMC) sampling consist of the most recent modelling techniques attempting to shrink the data dimension. Dimension reduction is generally addressed by shrinking a large set of model solutions (or combinations of predictors) in a smaller low-dimensional parameter space through either penalty functions or compressing regression models. The main aim behind these two approaches is to improve forecasting by dealing with some open variable selection problems such as overfitting, model uncertainty, and functional forms of misspecification.

In contributing to this discussion, papers submitted to this Special Issue would suggest computational approaches to make inference and perform forecasting with Big Data, focusing on the most relevant methodological improvements in the field of ML and Bayesian techniques.

Dr. Antonio Pacifico
Guest Editor

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• big data
• machine learning techniques
• MCMC implementations
• Bayesian methods
• forecasting