Hybrid structures in time series modeling and forecasting: A review
Z Hajirahimi, M Khashei - Engineering Applications of Artificial Intelligence, 2019 - Elsevier
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …
of single models in processing various patterns and relationships latent in data, hybrid …
Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach
In this paper, a new combined model coupling the linear autoregressive moving average
(ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed …
(ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed …
Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month …
Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-
spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory …
spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory …
Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm
X Zhang, J Wang, K Zhang - Electric Power Systems Research, 2017 - Elsevier
Short-term electric load forecasting (STLF) has been one of the most active areas of
research because of its vital role in planning and operation of power systems. Additionally …
research because of its vital role in planning and operation of power systems. Additionally …
Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries
Discussions about the recently identified deadly coronavirus disease (COVID-19) which
originated in Wuhan, China in December 2019 are common around the globe now. This is …
originated in Wuhan, China in December 2019 are common around the globe now. This is …
Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river
XH Nguyen - Advances in Water Resources, 2020 - Elsevier
Forecasting water level is an extremely important task as it allows to mitigate the effects of
floods, reduce and prevent disasters. Physically based models often give good results but …
floods, reduce and prevent disasters. Physically based models often give good results but …
EVDHM-ARIMA-based time series forecasting model and its application for COVID-19 cases
The time-series forecasting makes a substantial contribution in timely decision-making. In
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …
this article, a recently developed eigenvalue decomposition of Hankel matrix (EVDHM) …
[HTML][HTML] A methodology for electric power load forecasting
E Almeshaiei, H Soltan - Alexandria Engineering Journal, 2011 - Elsevier
Electricity demand forecasting is a central and integral process for planning periodical
operations and facility expansion in the electricity sector. Demand pattern is almost very …
operations and facility expansion in the electricity sector. Demand pattern is almost very …
Deep learning and transfer learning techniques applied to short-term load forecasting of data-poor buildings in local energy communities
ML Santos, SD García, X García-Santiago… - Energy and …, 2023 - Elsevier
The use of deep learning for electrical demand forecasting has shown great potential in
generating accurate results, but requires a large amount of data to train the models …
generating accurate results, but requires a large amount of data to train the models …
A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment
Infrastructure as a service (IaaS) providers are interested in increasing their profit by
gathering more and more customers besides providing more efficiency in cloud resource …
gathering more and more customers besides providing more efficiency in cloud resource …