1 citations · 2 across the 3 of their papers we have counts for
6 papers
The Impacts of Mobility on Covid-19 Dynamics: Using Soft and Hard Data
Leonardo Martins, Marcelo C. Medeiros
This paper has the goal of evaluating how changes in mobility has affected the infection spread of Covid-19 throughout the 2020-2021 years. However, identifying a "clean" causal re…
Modeling the Evolution of Infectious Diseases with Functional Data Models: The Case of COVID-19 in Brazil
Julian A. A. Collazos, Ronaldo Dias, Marcelo C. Medeiros
In this paper, we apply statistical methods for functional data to explain the heterogeneity in the evolution of number of deaths of Covid-19 over different regions. We treat the c…
The Proper Use of Google Trends in Forecasting Models
Marcelo C. Medeiros, Henrique F. Pires
It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many pap…
Machine Learning Advances for Time Series Forecasting
Ricardo P. Masini, Marcelo C. Medeiros, Eduardo F. Mendes
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alter…
Lockdown effects in US states: an artificial counterfactual approach
Carlos B. Carneiro, Iúri H. Ferreira, Marcelo C. Medeiros +2
We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explor…
BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions
Yuri Fonseca, Marcelo Medeiros, Gabriel Vasconcelos +1
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boos…