Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy
Authors
Keywords
-
Journal
European Journal of Health Economics
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2021-08-04
DOI
10.1007/s10198-021-01347-4
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Modeling and forecasting number of confirmed and death caused COVID-19 in IRAN: A comparison of time series forecasting methods
- (2021) Nasrin Talkhi et al. Biomedical Signal Processing and Control
- Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases
- (2021) Suyel Namasudra et al. NEURAL PROCESSING LETTERS
- COVID-19 Prevalence Forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey
- (2021) Gülhan Toğa et al. Journal of Infection and Public Health
- Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak
- (2020) Shi Zhao et al. INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES
- Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study
- (2020) Joseph T Wu et al. LANCET
- The Exponentially Increasing Rate of Patients Infected with COVID-19 in Iran
- (2020) Leila Moftakhar et al. Archives of Iranian Medicine
- Analysis and forecast of COVID-19 spreading in China, Italy and France
- (2020) Duccio Fanelli et al. CHAOS SOLITONS & FRACTALS
- Estimation of COVID-19 outbreak size in Italy
- (2020) Ashleigh R Tuite et al. LANCET INFECTIOUS DISEASES
- Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy
- (2020) Giulia Giordano et al. NATURE MEDICINE
- Wrong but Useful — What Covid-19 Epidemiologic Models Can and Cannot Tell Us
- (2020) Inga Holmdahl et al. NEW ENGLAND JOURNAL OF MEDICINE
- Estimation of reproduction numbers of COVID-19 in typical countries and epidemic trends under different prevention and control scenarios
- (2020) Chen Xu et al. Frontiers of Medicine
- Prevalence of Asymptomatic SARS-CoV-2 Infection
- (2020) Daniel P. Oran et al. ANNALS OF INTERNAL MEDICINE
- Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis
- (2020) Tanujit Chakraborty et al. CHAOS SOLITONS & FRACTALS
- Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil
- (2020) Matheus Henrique Dal Molin Ribeiro et al. CHAOS SOLITONS & FRACTALS
- Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19
- (2020) Sarbjit Singh et al. CHAOS SOLITONS & FRACTALS
- Estimation of COVID-19 prevalence in Italy, Spain, and France
- (2020) Zeynep Ceylan SCIENCE OF THE TOTAL ENVIRONMENT
- COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
- (2020) Vasilis Papastefanopoulos et al. Applied Sciences-Basel
- Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions
- (2020) Saleh I. Alzahrani et al. Journal of Infection and Public Health
- Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models
- (2020) Ovidiu-Dumitru Ilie et al. Microorganisms
- Comparative analysis and forecasting of COVID-19 cases in various European countries with ARIMA, NARNN and LSTM approaches
- (2020) İsmail Kırbaş et al. CHAOS SOLITONS & FRACTALS
- Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak
- (2020) Tridip Sardar et al. CHAOS SOLITONS & FRACTALS
- Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India
- (2020) Yongbin Wang et al. Infection and Drug Resistance
- Model uncertainty, political contestation, and public trust in science: Evidence from the COVID-19 pandemic
- (2020) S. E. Kreps et al. Science Advances
- COVID-19 Mortality Rate Prediction for India Using Statistical Neural Network Models
- (2020) S Dhamodharavadhani et al. Frontiers in Public Health
- A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece
- (2020) Christos Katris EXPERT SYSTEMS WITH APPLICATIONS
- The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors
- (2020) Gaetano Perone SCIENCE OF THE TOTAL ENVIRONMENT
- Forecasting for COVID-19 has failed
- (2020) John P.A. Ioannidis et al. INTERNATIONAL JOURNAL OF FORECASTING
- Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions
- (2018) Seyedeh Fallah et al. Energies
- A hybrid ETS–ANN model for time series forecasting
- (2017) Sibarama Panigrahi et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
- (2012) Alysha M. De Livera et al. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started