Hybrid structures in time series modeling and forecasting: A review
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 …
Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
Abstract The coronavirus disease 2019 (COVID-19) has become a public health emergency
of international concern affecting 201 countries and territories around the globe. As of April …
of international concern affecting 201 countries and territories around the globe. As of April …
Carbon price forecasting with a hybrid Arima and least squares support vector machines methodology
B Zhu, J Chevallier, B Zhu, J Chevallier - Pricing and forecasting carbon …, 2017 - Springer
This chapter advances a hybrid forecasting model for the carbon market. The technology is
based on Least Squares Support Vector Machines augmented by particle swarm …
based on Least Squares Support Vector Machines augmented by particle swarm …
A novel time series forecasting model with deep learning
Z Shen, Y Zhang, J Lu, J Xu, G **ao - Neurocomputing, 2020 - Elsevier
Time series forecasting is emerging as one of the most important branches of big data
analysis. However, traditional time series forecasting models can not effectively extract good …
analysis. However, traditional time series forecasting models can not effectively extract good …
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 …
Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression
B Zhu, D Han, P Wang, Z Wu, T Zhang, YM Wei - Applied energy, 2017 - Elsevier
Conventional methods are less robust in terms of accurately forecasting non-stationary and
nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based …
nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based …
A novel hybrid model combining βSARMA and LSTM for time series forecasting
Time series forecasting is an important and active research area due to the significance of
prediction and decision-making in several applications. Most commonly used models for …
prediction and decision-making in several applications. Most commonly used models for …
A hybrid system based on dynamic selection for time series forecasting
Hybrid systems, which combine statistical and machine learning (ML) techniques using
residual (error forecasting) modeling, have been highlighted in the literature due to their …
residual (error forecasting) modeling, have been highlighted in the literature due to their …
[HTML][HTML] Hybrid regression model for near real-time urban water demand forecasting
The most important factor in planning and operating water distribution systems is satisfying
consumer demand. This means continuously providing users with quality water in adequate …
consumer demand. This means continuously providing users with quality water in adequate …
Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion
Abstract Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare,
can lead to substantial improvements in Patient Care, Disease Management, Hospital …
can lead to substantial improvements in Patient Care, Disease Management, Hospital …