376 related articles for article (PubMed ID: 33311861)
1. 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]
2. 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]
3. 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]
4. 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]
5. Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy.
Gecili E; Ziady A; Szczesniak RD
PLoS One; 2021; 16(1):e0244173. PubMed ID: 33411744
[TBL] [Abstract][Full Text] [Related]
6. Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis.
Alharbi N
JMIRx Med; 2021; 2(1):e21044. PubMed ID: 34076627
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).
ArunKumar KE; Kalaga DV; Sai Kumar CM; Chilkoor G; Kawaji M; Brenza TM
Appl Soft Comput; 2021 May; 103():107161. PubMed ID: 33584158
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia.
Al-Turaiki I; Almutlaq F; Alrasheed H; Alballa N
Int J Environ Res Public Health; 2021 Aug; 18(16):. PubMed ID: 34444409
[TBL] [Abstract][Full Text] [Related]
11. Using meta-learning to recommend an appropriate time-series forecasting model.
Talkhi N; Akhavan Fatemi N; Jabbari Nooghabi M; Soltani E; Jabbari Nooghabi A
BMC Public Health; 2024 Jan; 24(1):148. PubMed ID: 38200512
[TBL] [Abstract][Full Text] [Related]
12. Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.
Shoaib M; Salahudin H; Hammad M; Ahmad S; Khan AA; Khan MM; Baig MAI; Ahmad F; Ullah MK
SN Comput Sci; 2021; 2(5):372. PubMed ID: 34258586
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.
Nguyen HM; Turk PJ; McWilliams AD
JMIR Public Health Surveill; 2021 Aug; 7(8):e28195. PubMed ID: 34346897
[TBL] [Abstract][Full Text] [Related]
15. Forecasting the number of confirmed new cases of COVID-19 in Italy for the period from 19 May to 2 June 2020.
Triacca M; Triacca U
Infect Dis Model; 2021; 6():362-369. PubMed ID: 33521404
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models.
Singh S; Murali Sundram B; Rajendran K; Boon Law K; Aris T; Ibrahim H; Chandra Dass S; Singh Gill B
J Infect Dev Ctries; 2020 Sep; 14(9):971-976. PubMed ID: 33031083
[TBL] [Abstract][Full Text] [Related]
19. Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021-2022.
Davidescu AA; Apostu SA; Paul A
Entropy (Basel); 2021 Mar; 23(3):. PubMed ID: 33803384
[TBL] [Abstract][Full Text] [Related]
20. Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario.
Ganiny S; Nisar O
Model Earth Syst Environ; 2021; 7(1):29-40. PubMed ID: 33490366
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]