Modern unorganized machines—extreme learning machines and echo state networks—
provide an elegant balance between processing capability and mathematical simplicity …

This work introduces a methodology to estimate coffee prices based on the use of Extreme
Learning Machines. The process is initiated by identifying the presence of nonstationary …

Streamflow series forecasting composes a fundamental step in planning electric energy
production for hydroelectric plants. In Brazil, such plants produce almost 70% of the total …

Estimating future streamflows is a key step in producing electricity for countries with
hydroelectric plants. Accurate predictions are particularly important due to environmental …

The forecasting of monthly seasonal streamflow time series is an important issue for
countries where hydroelectric plants contribute significantly to electric power generation …

This work performs an extensive investigation about the application of unorganized
machines–extreme learning machines and echo state networks–to predict monthly seasonal …

Feedforward neural networks are those in which the input signal follows only one direction:
from the input layer to the output layer, passing through all the hidden layers, in contrast with …

Time series forecasting problems are often addressed using linear techniques, especially
the autoregressive (AR) models, due to their simplicity combined with good performances. It …

The particulate matter PM 10 concentrations have been impacting hospital admissions due
to respiratory diseases. The air pollution studies seek to understand how this pollutant …

Several activities regarding water resources management are dependent on accurate
monthly streamflow forecasting, such as flood control, reservoir operation, water supply …