Paulo Canas Rodrigues
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View article: A Hybrid STL-Based Ensemble Model for PM2.5 Forecasting in Pakistani Cities
A Hybrid STL-Based Ensemble Model for PM2.5 Forecasting in Pakistani Cities Open
Air pollution, outstanding particulate matter (PM2.5), poses severe risks to human health and the environment in densely populated urban areas. Accurate short-term forecasting of PM2.5 concentrations is therefore crucial for timely public …
View article: A Novel Family of CDF Estimators Under PPS Sampling: Computational, Theoretical, and Applied Perspectives
A Novel Family of CDF Estimators Under PPS Sampling: Computational, Theoretical, and Applied Perspectives Open
Accurate estimation of population distribution characteristics is a fundamental task in survey sampling and statistical inference. This paper introduces a new family of estimators for the cumulative distribution function (CDF) under probab…
View article: Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection
Clinical Application of Machine Learning Models for Early-Stage Chronic Kidney Disease Detection Open
Background/Objectives: Chronic kidney disease (CKD) is a progressive condition that affects the body’s ability to remove waste and regulate fluid and electrolytes. Early detection is crucial for delaying disease progression and initiating …
View article: Global Shocks and Local Fragilities: A Financial Stress Index Approach to Pakistan’s Monetary and Asset Market Dynamics
Global Shocks and Local Fragilities: A Financial Stress Index Approach to Pakistan’s Monetary and Asset Market Dynamics Open
Economic stability in emerging market economies is increasingly shaped by the interplay between global financial integration, domestic monetary dynamics, and asset price fluctuations. Yet, early detection of financial market disruptions re…
View article: A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index
A Hybrid LMD–ARIMA–Machine Learning Framework for Enhanced Forecasting of Financial Time Series: Evidence from the NASDAQ Composite Index Open
This study proposes a novel hybrid forecasting approach designed explicitly for long-horizon financial time series. It incorporates LMD (Local Mean Decomposition), SD (Signal Decomposition), and sophisticated machine learning methods. The …
View article: A Hybrid Vector Autoregressive Model for Accurate Macroeconomic Forecasting: An Application to the U.S. Economy
A Hybrid Vector Autoregressive Model for Accurate Macroeconomic Forecasting: An Application to the U.S. Economy Open
Forecasting macroeconomic variables is essential to macroeconomics, financial economics, and monetary policy analysis. Due to the high dimensionality of the macroeconomic dataset, it is challenging to forecast efficiently and accurately. T…
View article: A robust-weighted AMMI modeling approach with generalized weighting schemes
A robust-weighted AMMI modeling approach with generalized weighting schemes Open
Funding Information: PCR acknowledges financial support from the Brazilian National Council for Scientific and Technological Development (CNPq), grant “Bolsa de produtividade PQ-2” 309359/2022-8, from the Federal University of Bahia, and C…
View article: Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application
Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application Open
Maintaining stable prices is one of the goals of monetary policy makers. Since its formation, inflation has been a key issue and priority for every Pakistani government; it is a fundamental macroeconomic variable that plays a significant r…
View article: Performance of Classification Algorithms Under Class Imbalance: Simulation and Real-World Evidence
Performance of Classification Algorithms Under Class Imbalance: Simulation and Real-World Evidence Open
Class imbalance is a persistent challenge in machine learning, particularly in high-stakes applications such as medical diagnostics, bioinformatics, and fraud detection, where the minority class often represents critical cases that require…
View article: Advancing Economic Forecasting With Hybrid Time Series and Functional Models: Evidence From Electricity Demand Data
Advancing Economic Forecasting With Hybrid Time Series and Functional Models: Evidence From Electricity Demand Data Open
Accurate short-term electricity demand forecasting (STDF) is critical not only for power system operational dependability but also for guiding strategic energy economic decision-making and promoting resilient, sustainable energy transition…
View article: An improved family of unbiased ratio estimators for a population distribution function
An improved family of unbiased ratio estimators for a population distribution function Open
This study discusses a novel family of unbiased ratio estimators using the Hartley-Ross (HR) method. The estimators are designed to estimate the population distribution function (PDF) in the context of simple random sampling with non-respo…
View article: Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique
Daily Crude Oil Prices Forecasting Using a Novel Hybrid Time Series Technique Open
This paper introduces a new hybrid time series forecasting technique to obtain an efficient and accurate daily crude oil prices forecast. The proposed hybrid technique combines the features of various regression, time series, and machine l…
View article: Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting
Hybrid Prophet-NAR Model for Short-Term Electricity Load Forecasting Open
Electricity load forecasting is crucial for effective energy management, particularly in minimizing energy production and distribution costs. Traditional models like SARIMA and Singular Spectrum Analysis (SSA) have been widely used but oft…
View article: Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks
Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks Open
Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into a set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are a family of inf…
View article: Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector
Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector Open
In Brazil, the industrial sector is the largest electricity consumer. Therefore, energy planning becomes important for industrial development. Electricity consumption data in the Brazilian industrial sector can be organized into a hierarch…
View article: Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting
Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting Open
Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized cleari…
View article: Indonesian Inflation Forecasting with Recurrent Neural Network Long Short-Term Memory (RNN-LSTM)
Indonesian Inflation Forecasting with Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) Open
This study forecasted inflation in Indonesia using the Recurrent Neural Network Long Short-Term Memory (RNN-LSTM) model, ideal for nonlinear, complex time series data. It evaluated the effects of different activation functions, such as Log…
View article: A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil
A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil Open
The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption ana…
View article: Hierarchical Time Series Forecasting of Fire Spots in Brazil: A Comprehensive Approach
Hierarchical Time Series Forecasting of Fire Spots in Brazil: A Comprehensive Approach Open
This study compares reconciliation techniques and base forecast methods to forecast a hierarchical time series of the number of fire spots in Brazil between 2011 and 2022. A three-level hierarchical time series was considered, comprising f…
View article: Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru
Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru Open
Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized …
View article: An exploratory analysis of PM$$_{2.5}$$/PM$$_{10}$$ ratio during spring 2016–2018 in Metropolitan Lima
An exploratory analysis of PM$$_{2.5}$$/PM$$_{10}$$ ratio during spring 2016–2018 in Metropolitan Lima Open
Aerosols (PM $$_{2.5}$$ and PM $$_{10}$$ ) represent one of the most critical pollutants due to their negative effects on human health. This research analyzed the relationship of PM and its PM $$_{2.5}$$ /PM $$_{10}$$ ratios …
View article: Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange
Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange Open
Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a uniq…
View article: A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification
A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification Open
Within the e-commerce sphere, optimizing the product classification process assumes pivotal importance, owing to its direct influence on operational efficiency and profitability. In this context, employing machine learning algorithms stand…
View article: Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru
Multi-step ahead ozone level forecasting using a component-based technique: A case study in Lima, Peru Open
The rise in global ozone levels over the last few decades has harmed human health. This problem exists in several cities throughout South America due to dangerous levels of particulate matter in the air, particularly during the winter seas…
View article: Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values
Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values Open
Forecasting air pollutant levels is essential in regulatory plans focused on controlling and mitigating air pollutants, such as particulate matter. Focusing the forecast on air pollution peaks is challenging and complex since the pollutant…