Making the whole greater than the sum of its parts: A literature review of ensemble methods for financial time series forecasting
This paper discusses the application of ensemble techniques for the prediction of time
series, presenting an in‐depth review of the main techniques and algorithms used by the …
series, presenting an in‐depth review of the main techniques and algorithms used by the …
A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran
A Sharafati, SBHS Asadollah, A Neshat - Journal of Hydrology, 2020 - Elsevier
This study presents a new strategy to predict the monthly groundwater level with short-and
long-lead times over the Rafsanjan aquifer in Iran using an ensemble machine learning …
long-lead times over the Rafsanjan aquifer in Iran using an ensemble machine learning …
ML-based pre-deployment SDN performance prediction with neural network boosting regression
Software defined networking (SDN) has been proposed as an effective approach to improve
network management efficiency and increase network intelligence in various networks …
network management efficiency and increase network intelligence in various networks …
Detecting botnet by using particle swarm optimization algorithm based on voting system
Botnets have recently been identified as serious Internet threats that are continually
developing and expanding. Identifying botnets in the domain of network security is regarded …
developing and expanding. Identifying botnets in the domain of network security is regarded …
Application of newly developed ensemble machine learning models for daily suspended sediment load prediction and related uncertainty analysis
A Sharafati, SB Haji Seyed Asadollah… - Hydrological …, 2020 - Taylor & Francis
Ensemble machine learning models have been widely used in hydro-systems modeling as
robust prediction tools that combine multiple decision trees. In this study, three newly …
robust prediction tools that combine multiple decision trees. In this study, three newly …
IoT traffic prediction using multi-step ahead prediction with neural network
AR Abdellah, OAK Mahmood… - … Congress on Ultra …, 2019 - ieeexplore.ieee.org
Internet of Things (IoT) is a network of interconnected devices, such as sensors and smart
devices that have processing, sensing, and communication capabilities, as well as can …
devices that have processing, sensing, and communication capabilities, as well as can …
[HTML][HTML] Electricity demand forecasting in industrial and residential facilities using ensemble machine learning
R Porteiro, L Hernández-Callejo… - Revista Facultad de …, 2022 - scielo.org.co
This article presents electricity demand forecasting models for industrial and residential
facilities, developed using ensemble machine learning strategies. Short term electricity …
facilities, developed using ensemble machine learning strategies. Short term electricity …
Model-free short-term fluid dynamics estimator with a deep 3D-convolutional neural network
Deep learning models are not yet fully applied to fluid dynamics predictions, while they are
the state-of-the-art solution in many other areas ie video and language processing, finance …
the state-of-the-art solution in many other areas ie video and language processing, finance …
IoT type-of-traffic forecasting method based on gradient boosting neural networks
M Lopez-Martin, B Carro… - Future Generation …, 2020 - Elsevier
Network traffic classification is an important task for any current data network. There any
many possible classification targets for the traffic, but we have considered as especially …
many possible classification targets for the traffic, but we have considered as especially …
Toward QoS prediction based on temporal transformers for IoT applications
Internet of Things (IoT) devices generate a tremendous amount of time series data that is
extremely dynamic, heterogeneous and time dependent. Such types of data introduce …
extremely dynamic, heterogeneous and time dependent. Such types of data introduce …