Application of Artificial Neural Networks for Short-Term Forecasting of Container Flows in Kazakhstan

2019 ◽  
pp. 131-158
Author(s):  
Zhomart Abdirassilov ◽  
Aleksander Sładkowski ◽  
Aliya Izbairova ◽  
Sugerali Sarbaev
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting

Electricity Price and Load Short-Term Forecasting Using Artificial Neural Networks

ENERGYO ◽  
2018 ◽  
Author(s):  
Paras Mandal ◽  
Tomonobu Senjyu ◽  
Naomitsu Urasaki ◽  
Toshihisa Funabashi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Electricity Price ◽  
Short Term ◽  
Artificial Neural ◽  
Short Term Forecasting

scholarly journals Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248161
Author(s):  
Marcus de Barros Braga ◽  
Rafael da Silva Fernandes ◽  
Gilberto Nerino de Souza ◽  
Jonas Elias Castro da Rocha ◽  
Cícero Jorge Fonseca Dolácio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Brazilian Amazon ◽  
Government Officials ◽  
Short Term ◽  
First Case ◽  
Hospital Beds ◽  
Artificial Neural ◽  
Short Term Forecasting ◽  
Novel Coronavirus

The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

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