A survey on 5G radio access network energy efficiency: Massive MIMO, lean carrier design, sleep modes, and machine learning
D López-Pérez, A De Domenico… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Cellular networks have changed the world we are living in, and the fifth generation (5G) of
radio technology is expected to further revolutionise our everyday lives by enabling a high …
radio technology is expected to further revolutionise our everyday lives by enabling a high …
Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …
energy systems and economics. Creating a forecasting model for a specific use case …
An optimized model using LSTM network for demand forecasting
H Abbasimehr, M Shabani, M Yousefi - Computers & industrial engineering, 2020 - Elsevier
In a business environment with strict competition among firms, accurate demand forecasting
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
is not straightforward. In this paper, a forecasting method is proposed, which has a strong …
Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities
Sports facilities are considered complex buildings due to their high energy demand and
occupancy profiles. Therefore, their management and optimization are crucial for reducing …
occupancy profiles. Therefore, their management and optimization are crucial for reducing …
Improving time series forecasting using LSTM and attention models
H Abbasimehr, R Paki - Journal of Ambient Intelligence and Humanized …, 2022 - Springer
Accurate time series forecasting has been recognized as an essential task in many
application domains. Real-world time series data often consist of non-linear patterns with …
application domains. Real-world time series data often consist of non-linear patterns with …
[HTML][HTML] Forecasting container freight rates using the Prophet forecasting method
This study applies three innovative methods in forecasting container freight rates. Firstly, we
extracted 471 major disruptive events from the 'Lloyds List'database from 2010 until 2020 …
extracted 471 major disruptive events from the 'Lloyds List'database from 2010 until 2020 …
[HTML][HTML] Strategies for time series forecasting with generalized regression neural networks
This paper discusses how to forecast time series using generalized regression neural
networks. The main goal is to take advantage of their inherent properties to generate fast …
networks. The main goal is to take advantage of their inherent properties to generate fast …
[HTML][HTML] Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana
C Twumasi, J Twumasi - International Journal of Forecasting, 2022 - Elsevier
The major challenge in managing blood products lies in the uncertainty of blood demand
and supply, with a trade-off between shortage and wastage, especially in most developing …
and supply, with a trade-off between shortage and wastage, especially in most developing …
Forecasting campground demand in US national parks
Camping has grown from a recreational activity to an emerging tourism sector. In America's
national parks, this growth is amplified by increasing visitation and an occupancy limited by …
national parks, this growth is amplified by increasing visitation and an occupancy limited by …
Improving sporadic demand forecasting using a modified k-nearest neighbor framework
Forecasting sporadic or intermittent demand presents significant challenges in supply chain
management, primarily due to the frequent occurrence of zero demand values and the …
management, primarily due to the frequent occurrence of zero demand values and the …