Time series forecasting

Hybrid machine learning models applied to daily urban water consumption prediction

This study proposes hybrid machine learning (ML) models to predict the daily urban water consumption scenario in a neighborhood Brazilian city. The framework evaluates various signal decomposition modes, including empirical wavelet transform (EWT), …

Signal decomposition and stacking-ensemble learning approaches applied to time series forecasting

Time series forecasting is an essential approach for businesses and researchers to make informed decisions by predicting future trends and patterns in a given time series data. Nevertheless, forecasting time series accurately can be challenging due …

Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach

Wind energy is an emerging source of renewable energy in Brazil. Nevertheless, it already accounts for 17% of the National Interconnected Network. Due to the great intricacy of wind speed variations, it is difficult to predict wind energy with high …

Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting

The use of wind energy plays a vital role in society owing to its economic and environmental importance. Knowing the wind power generation within a specific time window is useful for facilitating decision making in terms of maintenance, electricity …

Dengue cases forecasting based on extreme gradient boosting ensemble with coyote optimization

Dengue is considered a public health problem in tropical regions, periodically affecting an increasing number of citizens. Consequently, the development of efficient models is essentials to short and long-term forecasting, supporting health care …

An improved ensemble learning model for multi-step ahead wind power generation forecasting

The development and expansion of clean energy, such as wind energy, are important in the preservation of the environment and development of local economies and an alternative to hydroelectric and thermal energies. In this respect, the development of …

Multi-step wind speed forecasting based on multi-stage decomposition approach

Wind energy is one of the sources which is still in development in Brazil, however, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, prediction of wind speed with …

Wind Energy Multi-Step Ahead Forecasting Based on Variational Mode Decomposition

Wind energy is one of the sources which still in development in Brazil, however, it already represents 17% of the National Interconnected System. Due to the high level of uncertainty and fluctuations in wind speed, prediction of wind energy with high …

Short-term forecasting of Amazon rainforest fires based on ensemble decomposition model

Accurate forecasting is important for decision-makers. Recently, the Amazon rainforest is reaching record levels of the number of fires, a situation that concerns both climate and public health problems. Obtaining the desired forecasting accuracy …