156 related articles for article (PubMed ID: 38315901)
21. Time series model for forecasting the number of new admission inpatients.
Zhou L; Zhao P; Wu D; Cheng C; Huang H
BMC Med Inform Decis Mak; 2018 Jun; 18(1):39. PubMed ID: 29907102
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA.
Qi C; Zhang D; Zhu Y; Liu L; Li C; Wang Z; Li X
BMC Med Res Methodol; 2020 Sep; 20(1):243. PubMed ID: 32993517
[TBL] [Abstract][Full Text] [Related]
24. Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011-2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models.
Liu H; Li C; Shao Y; Zhang X; Zhai Z; Wang X; Qi X; Wang J; Hao Y; Wu Q; Jiao M
J Infect Public Health; 2020 Feb; 13(2):287-294. PubMed ID: 31953020
[TBL] [Abstract][Full Text] [Related]
25. A hybrid seasonal prediction model for tuberculosis incidence in China.
Cao S; Wang F; Tam W; Tse LA; Kim JH; Liu J; Lu Z
BMC Med Inform Decis Mak; 2013 May; 13():56. PubMed ID: 23638635
[TBL] [Abstract][Full Text] [Related]
26. Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province.
Yang W; Su A; Ding L
BMC Public Health; 2023 Nov; 23(1):2309. PubMed ID: 37993836
[TBL] [Abstract][Full Text] [Related]
27. Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.
Wang Z; Wang Y; Zhang S; Wang S; Xu Z; Feng Z
BMC Infect Dis; 2024 Jan; 24(1):113. PubMed ID: 38253998
[TBL] [Abstract][Full Text] [Related]
28. Short-term traffic speed prediction under different data collection time intervals using a SARIMA-SDGM hybrid prediction model.
Song Z; Guo Y; Wu Y; Ma J
PLoS One; 2019; 14(6):e0218626. PubMed ID: 31242226
[TBL] [Abstract][Full Text] [Related]
29. Predictive determinants of scorpion stings in a tropical zone of south Iran: use of mixed seasonal autoregressive moving average model.
Ebrahimi V; Hamdami E; Moemenbellah-Fard MD; Ezzatzadegan Jahromi S
J Venom Anim Toxins Incl Trop Dis; 2017; 23():39. PubMed ID: 28852405
[TBL] [Abstract][Full Text] [Related]
30. Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China.
Wang YB; Qing SY; Liang ZY; Ma C; Bai YC; Xu CJ
World J Gastroenterol; 2023 Nov; 29(42):5716-5727. PubMed ID: 38075851
[TBL] [Abstract][Full Text] [Related]
31. 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]
32. Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.
Tohidinik HR; Mohebali M; Mansournia MA; Niakan Kalhori SR; Ali-Akbarpour M; Yazdani K
Trop Med Int Health; 2018 Aug; 23(8):860-869. PubMed ID: 29790236
[TBL] [Abstract][Full Text] [Related]
33. Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.
Zhao D; Zhang H; Zhang R; He S
BMC Public Health; 2023 Mar; 23(1):619. PubMed ID: 37003988
[TBL] [Abstract][Full Text] [Related]
34. Forecasting deaths of road traffic injuries in China using an artificial neural network.
Qian Y; Zhang X; Fei G; Sun Q; Li X; Stallones L; Xiang H
Traffic Inj Prev; 2020; 21(6):407-412. PubMed ID: 32500738
[No Abstract] [Full Text] [Related]
35. An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China.
Wang Y; Xu C; Li Y; Wu W; Gui L; Ren J; Yao S
Infect Drug Resist; 2020; 13():867-880. PubMed ID: 32273731
[TBL] [Abstract][Full Text] [Related]
36. Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China.
Zheng Y; Zhang L; Wang L; Rifhat R
BMC Infect Dis; 2020 Apr; 20(1):300. PubMed ID: 32321419
[TBL] [Abstract][Full Text] [Related]
37. Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.
Wu WW; Li Q; Tian DC; Zhao H; Xia Y; Xiong Y; Su K; Tang WG; Chen X; Wang J; Qi L
Epidemiol Infect; 2022 Apr; 150():e90. PubMed ID: 35543101
[TBL] [Abstract][Full Text] [Related]
38. A hybrid model for short-term bacillary dysentery prediction in Yichang City, China.
Yan W; Xu Y; Yang X; Zhou Y
Jpn J Infect Dis; 2010 Jul; 63(4):264-70. PubMed ID: 20657066
[TBL] [Abstract][Full Text] [Related]
39. Time-series analysis of tuberculosis from 2005 to 2017 in China.
Wang H; Tian CW; Wang WM; Luo XM
Epidemiol Infect; 2018 Jun; 146(8):935-939. PubMed ID: 29708082
[TBL] [Abstract][Full Text] [Related]
40. 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]
[Previous] [Next] [New Search]