Erick Meira

Erick Meira

Lecturer of Time Series Analysis and Forecasting

Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

Biography

Erick Meira graduated with honors (cum laude) from the Federal University of Rio de Janeiro with a B. Eng. in Oil and Gas Engineering, having also participated in a six-month academic programme at École Nationale Supérieure des Mines d’Alès (France). He also holds MSc and PhD degrees in Industrial Engineering from the Pontifical Catholic University of Rio de Janeiro Janeiro (PUC-Rio) - minor in Time Series Analysis and Forecasting. Most recently, Erick was also a Visiting Scholar at the University of Bath (School of Management).

Permanent employee, approved by public tender, of the Brazilian Agency for Research and Innovation (Finep), a federal, state-owned company whose mission is to promote the social and economic development through the public fostering of Science, Technology and Innovation (STI). Currently working at the Energy, Information Technology and Services Division (DETI). Main attributions: to promote and strengthen innovation projects in several priority areas defined by the Ministry of Science, Technology, Innovations and Communications (MCTIC), particularly, Renewable Energies, Energy Efficiency, Sustainable Production, Water Resources, among others.

He possesses emphasized working experience on Industrial Engineering, Data Science and Finances, with major interests in the following subjects: Time Series, Forecasting, Data Analytics, Statistical Learning, Financial Econometrics and Sustainable Finance. [Scopus ID: 57191618655 (de Oliveira, Erick Meira); ORCID: 0000-0002-8555-3880; ResearcherID: H-3277-2014]

Interests

  • Time Series
  • Forecasting
  • Financial Econometrics
  • Sustainable Finance

Education

  • Ph.D. in Industrial Engineering, 2020

    Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

  • M.Sc. in Industrial Engineering, 2015

    Pontifical Catholic University of Rio de Janeiro (PUC-Rio)

  • BSc in Oil & Gas Engineering, 2012

    Federal University of Rio de Janeiro (UFRJ)

Accomplish­ments

Neural Networks and Deep Learning

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Blockchain Fundamentals

Formulated informed blockchain models, hypotheses, and use cases.
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Object-Oriented Programming in R: S3 and R6 Course

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Recent Posts

Recent & Upcoming Talks

Recent Publications

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Treating and Pruning: new approaches to forecasting model selection and combination using prediction intervals

We introduce a new way of selecting among model forms in automated ETS forecasting routines, here addressed as treating. The approach operates by subsetting the pool of competing models based on the information delivered by their prediction intervals. An application to exponential smoothing formulations gave rise to alternative forecasting methods, the Treated ETS and the Treated AICc weights. By the same token, we also proposed a pruning strategy that can be used to enhance the accuracy of forecasts arising from any forecast combination method, provided that the models to be combined are able to generate prediction intervals to their point forecasts.

On the effects of uncertainty measures on sustainability indices: An empirical investigation in a nonlinear framework

The short and long-run effects of uncertainty measures on the dynamic of sustainability indices are investigated.

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Contact

  • Rua Marquês de São Vicente, 225, Rio de Janeiro, RJ 22451-900
  • IAG Business School
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