Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA–NNAR hybrid model
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA–NNAR hybrid model
Authors
Keywords
Forecasting, Hand, foot and mouth disease, Artificial neural networks, Neural networks, Autocorrelation, Computer software, Epidemiology, Preprocessing
Journal
PLoS One
Volume 16, Issue 2, Pages e0246673
Publisher
Public Library of Science (PLoS)
Online
2021-02-08
DOI
10.1371/journal.pone.0246673
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Application of a combined model with seasonal autoregressive integrated moving average and support vector regression in forecasting hand-foot-mouth disease incidence in Wuhan, China
- (2019) Jiao-Jiao Zou et al. MEDICINE
- Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study
- (2019) Ya-wen Wang et al. BMJ Open
- Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China
- (2019) Wendong Liu et al. BMC INFECTIOUS DISEASES
- The Clinical and Epidemiological Study of Children with Hand, Foot, and Mouth Disease in Hunan, China from 2013 to 2017
- (2019) Jun Qiu et al. Scientific Reports
- Time series model for forecasting the number of new admission inpatients
- (2018) Lingling Zhou et al. BMC Medical Informatics and Decision Making
- Time-series analysis of tuberculosis from 2005 to 2017 in China
- (2018) H. Wang et al. EPIDEMIOLOGY AND INFECTION
- Epidemiological Characteristics and Spatial-Temporal Distribution of Hand, Foot, and Mouth Disease in Chongqing, China, 2009–2016
- (2018) Li Qi et al. International Journal of Environmental Research and Public Health
- Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model
- (2018) Yongbin Wang et al. PLoS One
- Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network
- (2017) K. W. WANG et al. EPIDEMIOLOGY AND INFECTION
- Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability
- (2017) Sijun Liu et al. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
- Hand, Foot, and Mouth Disease in China: Modeling Epidemic Dynamics of Enterovirus Serotypes and Implications for Vaccination
- (2016) Saki Takahashi et al. PLOS MEDICINE
- Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model
- (2015) L. LIU et al. EPIDEMIOLOGY AND INFECTION
- Epidemiological Research on Hand, Foot, and Mouth Disease in Mainland China
- (2015) Zhi-Chao Zhuang et al. Viruses-Basel
- Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks
- (2015) Junghwan Jin et al. PLoS One
- Time Series Analysis of Hand-Foot-Mouth Disease Hospitalization in Zhengzhou: Establishment of Forecasting Models Using Climate Variables as Predictors
- (2014) Huifen Feng et al. PLoS One
- Application of a New Hybrid Model with Seasonal Auto-Regressive Integrated Moving Average (ARIMA) and Nonlinear Auto-Regressive Neural Network (NARNN) in Forecasting Incidence Cases of HFMD in Shenzhen, China
- (2014) Lijing Yu et al. PLoS One
- Chlorophyll a Simulation in a Lake Ecosystem Using a Model with Wavelet Analysis and Artificial Neural Network
- (2013) Fei Wang et al. ENVIRONMENTAL MANAGEMENT
- An enhanced hybrid method for time series prediction using linear and neural network models
- (2012) Purwanto et al. APPLIED INTELLIGENCE
- A new linear & nonlinear artificial neural network model for time series forecasting
- (2012) Ufuk Yolcu et al. DECISION SUPPORT SYSTEMS
- Wavelet analysis of ecological time series
- (2008) Bernard Cazelles et al. OECOLOGIA
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started