Time series-based workload prediction using the statistical hybrid model for the cloud environment
Resource management is addressed using infrastructure as a service. On demand, the
resource management module effectively manages available resources. Resource …
resource management module effectively manages available resources. Resource …
Using the SARIMA model to forecast the fourth global wave of cumulative deaths from COVID-19: Evidence from 12 hard-hit big countries
G Perone - Econometrics, 2022 - mdpi.com
The COVID-19 pandemic is a serious threat to all of us. It has caused an unprecedented
shock to the world's economy, and it has interrupted the lives and livelihood of millions of …
shock to the world's economy, and it has interrupted the lives and livelihood of millions of …
Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain
This study introduces a forecasting model to help design an effective blood supply chain
mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people …
mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people …
[PDF][PDF] Optimizing Cloud Load Forecasting with a CNN-BiLSTM Hybrid Model
V Ramamoorthi - … Journal of Intelligent Automation and Computing, 2022 - researchgate.net
Cloud computing has emerged as a cornerstone for modern industries, offering scalable and
flexible resources to meet growing computational demands. However, managing fluctuating …
flexible resources to meet growing computational demands. However, managing fluctuating …
Covid-19 forecasting model based on machine learning approaches: a review
Abstract As coronavirus disease (Covid-19) it is a contagious disease that is spread by the
SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was …
SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was …
A dynamic ensemble approach based on trend analysis to COVID-19 incidence forecast
JP de Sales, PSG de Mattos Neto… - … Signal Processing and …, 2024 - Elsevier
Traditional models have struggled to effectively predict COVID-19 incidence time series.
Limitation arises from their difficulty in accurately capturing complex behaviors in COVID-19 …
Limitation arises from their difficulty in accurately capturing complex behaviors in COVID-19 …
TransLearn: A clustering based knowledge transfer strategy for improved time series forecasting
The widespread usage of time series prediction has led to the formulation of several hybrid
and statistical algorithms. These algorithms assume the availability of significant quantities …
and statistical algorithms. These algorithms assume the availability of significant quantities …
Multivariate time series ensemble model for load prediction on hosts using anomaly detection techniques
Host load prediction is essential in computing to improve resource utilization and for
achieving service level agreements. However, due to variations in load and the inefficiency …
achieving service level agreements. However, due to variations in load and the inefficiency …
[HTML][HTML] An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic
Quantifying and analyzing excess mortality in crises such as the ongoing COVID-19
pandemic is crucial for policymakers. Traditional measures fail to take into account …
pandemic is crucial for policymakers. Traditional measures fail to take into account …
Forecasting Implementation of Hybrid Time Series and Artificial Neural Network Models
DL Polestico, AL Bangcale, LC Velasco - Procedia Computer Science, 2024 - Elsevier
This study implemented AR, SARIMA, and SETAR models and their hybrid with ANN using
the Canadian lynx data. Implementing a SETAR-ANN has been shown to be successful in …
the Canadian lynx data. Implementing a SETAR-ANN has been shown to be successful in …