118 related articles for article (PubMed ID: 36778724)
1. Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach.
Annamalai N; Johnson A
SN Comput Sci; 2023; 4(2):193. PubMed ID: 36778724
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
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. 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]
11. Short and Long term predictions of Hospital emergency department attendances.
Jilani T; Housley G; Figueredo G; Tang PS; Hatton J; Shaw D
Int J Med Inform; 2019 Sep; 129():167-174. PubMed ID: 31445251
[TBL] [Abstract][Full Text] [Related]
12. Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China.
Wang G; Wei W; Jiang J; Ning C; Chen H; Huang J; Liang B; Zang N; Liao Y; Chen R; Lai J; Zhou O; Han J; Liang H; Ye L
Epidemiol Infect; 2019 Jan; 147():e194. PubMed ID: 31364559
[TBL] [Abstract][Full Text] [Related]
13. Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.
Ye GH; Alim M; Guan P; Huang DS; Zhou BS; Wu W
PLoS One; 2021; 16(3):e0248597. PubMed ID: 33725011
[TBL] [Abstract][Full Text] [Related]
14. Performance of time-series methods in forecasting the demand for red blood cell transfusion.
Pereira A
Transfusion; 2004 May; 44(5):739-46. PubMed ID: 15104656
[TBL] [Abstract][Full Text] [Related]
15. Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models.
Patil S; Pandya S
Front Public Health; 2021; 9():798034. PubMed ID: 34900929
[TBL] [Abstract][Full Text] [Related]
16. Forecasting Patient Discharge Before Noon: A Comparison Between Holt's and Box-Jenkins' Models.
Berríos RA
Qual Manag Health Care; 2019; 28(4):237-244. PubMed ID: 31567847
[TBL] [Abstract][Full Text] [Related]
17. Forecasting daily maximum surface ozone concentrations in Brunei Darussalam--an ARIMA modeling approach.
Kumar K; Yadav AK; Singh MP; Hassan H; Jain VK
J Air Waste Manag Assoc; 2004 Jul; 54(7):809-14. PubMed ID: 15303293
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Time Series Prediction of Lung Cancer Death Rates on the Basis of SEER Data.
Altuhaifa F
JCO Clin Cancer Inform; 2023 Jun; 7():e2300011. PubMed ID: 37311162
[TBL] [Abstract][Full Text] [Related]
20. Modeling the number of unemployed in South Sumatra Province using the exponential smoothing methods.
Gustriansyah R; Alie J; Suhandi N
Qual Quant; 2023; 57(2):1725-1737. PubMed ID: 35694111
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
