216 related articles for article (PubMed ID: 35747601)
1. Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data.
Daniyal M; Tawiah K; Muhammadullah S; Opoku-Ameyaw K
J Healthc Eng; 2022; 2022():4802743. PubMed ID: 35747601
[TBL] [Abstract][Full Text] [Related]
2. A COVID-19 Pandemic Artificial Intelligence-Based System With Deep Learning Forecasting and Automatic Statistical Data Acquisition: Development and Implementation Study.
Yu CS; Chang SS; Chang TH; Wu JL; Lin YJ; Chien HF; Chen RJ
J Med Internet Res; 2021 May; 23(5):e27806. PubMed ID: 33900932
[TBL] [Abstract][Full Text] [Related]
3. A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020.
Zenia S; L'Hadj M; Selmane S
J Res Health Sci; 2023 Sep; 23(3):e00586. PubMed ID: 38315901
[TBL] [Abstract][Full Text] [Related]
4. Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases.
Qureshi M; Daniyal M; Tawiah K
J Healthc Eng; 2022; 2022():4864920. PubMed ID: 36406332
[TBL] [Abstract][Full Text] [Related]
5. Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy.
Perone G
Eur J Health Econ; 2022 Aug; 23(6):917-940. PubMed ID: 34347175
[TBL] [Abstract][Full Text] [Related]
6. Time series prediction of under-five mortality rates for Nigeria: comparative analysis of artificial neural networks, Holt-Winters exponential smoothing and autoregressive integrated moving average models.
Adeyinka DA; Muhajarine N
BMC Med Res Methodol; 2020 Dec; 20(1):292. PubMed ID: 33267817
[TBL] [Abstract][Full Text] [Related]
7. Epidemiology and time series analysis of human brucellosis in Tebessa province, Algeria, from 2000 to 2020.
Akermi SE; L'Hadj M; Selmane S
J Res Health Sci; 2022 Mar; 22(1):e00544. PubMed ID: 36511254
[TBL] [Abstract][Full Text] [Related]
8. Forecasting COVID-19 in Pakistan.
Ali M; Khan DM; Aamir M; Khalil U; Khan Z
PLoS One; 2020; 15(11):e0242762. PubMed ID: 33253248
[TBL] [Abstract][Full Text] [Related]
9. Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.
Almarashi AM; Daniyal M; Jamal F
PLoS One; 2024; 19(2):e0297180. PubMed ID: 38394105
[TBL] [Abstract][Full Text] [Related]
10. A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance.
Almarashi AM; Daniyal M; Jamal F
BMC Sports Sci Med Rehabil; 2024 Jan; 16(1):28. PubMed ID: 38273407
[TBL] [Abstract][Full Text] [Related]
11. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.
Azeez A; Obaromi D; Odeyemi A; Ndege J; Muntabayi R
Int J Environ Res Public Health; 2016 Jul; 13(8):. PubMed ID: 27472353
[TBL] [Abstract][Full Text] [Related]
12. 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.
Wang YW; Shen ZZ; Jiang Y
BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084
[TBL] [Abstract][Full Text] [Related]
13. Forecasting of daily new lumpy skin disease cases in Thailand at different stages of the epidemic using fuzzy logic time series, NNAR, and ARIMA methods.
Punyapornwithaya V; Arjkumpa O; Buamithup N; Kuatako N; Klaharn K; Sansamur C; Jampachaisri K
Prev Vet Med; 2023 Aug; 217():105964. PubMed ID: 37393704
[TBL] [Abstract][Full Text] [Related]
14. Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model.
Yadav BK; Srivastava SK; Arasu PT; Singh P
Can J Infect Dis Med Microbiol; 2023; 2023():5934552. PubMed ID: 38144388
[TBL] [Abstract][Full Text] [Related]
15. Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.
Singh RK; Rani M; Bhagavathula AS; Sah R; Rodriguez-Morales AJ; Kalita H; Nanda C; Sharma S; Sharma YD; Rabaan AA; Rahmani J; Kumar P
JMIR Public Health Surveill; 2020 May; 6(2):e19115. PubMed ID: 32391801
[TBL] [Abstract][Full Text] [Related]
16. Forecasting COVID-19 pandemic using optimal singular spectrum analysis.
Kalantari M
Chaos Solitons Fractals; 2021 Jan; 142():110547. PubMed ID: 33311861
[TBL] [Abstract][Full Text] [Related]
17. Application of one-, three-, and seven-day forecasts during early onset on the COVID-19 epidemic dataset using moving average, autoregressive, autoregressive moving average, autoregressive integrated moving average, and naïve forecasting methods.
Lynch CJ; Gore R
Data Brief; 2021 Apr; 35():106759. PubMed ID: 33521186
[TBL] [Abstract][Full Text] [Related]
18. [A comparative study of time series models in predicting COVID-19 cases].
Li ZQ; Tao B; Zhan M; Wu Z; Wu J; Wang J
Zhonghua Liu Xing Bing Xue Za Zhi; 2021 Mar; 42(3):421-426. PubMed ID: 34814408
[No Abstract] [Full Text] [Related]
19. Short-Range Forecasting of COVID-19 During Early Onset at County, Health District, and State Geographic Levels Using Seven Methods: Comparative Forecasting Study.
Lynch CJ; Gore R
J Med Internet Res; 2021 Mar; 23(3):e24925. PubMed ID: 33621186
[TBL] [Abstract][Full Text] [Related]
20. Analyzing and Forecasting Pediatric Fever Clinic Visits in High Frequency Using Ensemble Time-Series Methods After the COVID-19 Pandemic in Hangzhou, China: Retrospective Study.
Zhang W; Zhu Z; Zhao Y; Li Z; Chen L; Huang J; Li J; Yu G
JMIR Med Inform; 2023 Sep; 11():e45846. PubMed ID: 37728972
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
