141 related articles for article (PubMed ID: 35595895)
1. Wastewater inflow time series forecasting at low temporal resolution using SARIMA model: a case study in South Australia.
Do P; Chow CWK; Rameezdeen R; Gorjian N
Environ Sci Pollut Res Int; 2022 Oct; 29(47):70984-70999. PubMed ID: 35595895
[TBL] [Abstract][Full Text] [Related]
2. Seasonal autoregressive integrated moving average (SARIMA) time-series model for milk production forecasting in pasture-based dairy cows in the Andean highlands.
Perez-Guerra UH; Macedo R; Manrique YP; Condori EA; Gonzáles HI; Fernández E; Luque N; Pérez-Durand MG; García-Herreros M
PLoS One; 2023; 18(11):e0288849. PubMed ID: 37972120
[TBL] [Abstract][Full Text] [Related]
3. Applying SARIMA, ETS, and hybrid models for prediction of tuberculosis incidence rate in Taiwan.
Kuan MM
PeerJ; 2022; 10():e13117. PubMed ID: 36164599
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China.
Zhao D; Zhang R
J Infect Dev Ctries; 2023 Nov; 17(11):1581-1590. PubMed ID: 38064398
[TBL] [Abstract][Full Text] [Related]
8. Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness.
Orang A; Berke O; Poljak Z; Greer AL; Rees EE; Ng V
Zoonoses Public Health; 2024 May; 71(3):304-313. PubMed ID: 38331569
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.
Wang Y; Xu C; Zhang S; Wang Z; Yang L; Zhu Y; Yuan J
BMJ Open; 2019 Jul; 9(7):e024409. PubMed ID: 31371283
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. 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]
14. Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq.
Mohammed SH; Ahmed MM; Al-Mousawi AM; Azeez A
Int J Mycobacteriol; 2018; 7(4):361-367. PubMed ID: 30531036
[TBL] [Abstract][Full Text] [Related]
15. Forecasting the incidence of mumps in Chongqing based on a SARIMA model.
Qiu H; Zhao H; Xiang H; Ou R; Yi J; Hu L; Zhu H; Ye M
BMC Public Health; 2021 Feb; 21(1):373. PubMed ID: 33596871
[TBL] [Abstract][Full Text] [Related]
16. 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.
Zou JJ; Jiang GF; Xie XX; Huang J; Yang XB
Medicine (Baltimore); 2019 Feb; 98(6):e14195. PubMed ID: 30732135
[TBL] [Abstract][Full Text] [Related]
17. Forecasting daily emergency department visits using calendar variables and ambient temperature readings.
Marcilio I; Hajat S; Gouveia N
Acad Emerg Med; 2013 Aug; 20(8):769-77. PubMed ID: 24033619
[TBL] [Abstract][Full Text] [Related]
18. Good times bad times: Automated forecasting of seasonal cryptosporidiosis in Ontario using machine learning.
Berke O; Trotz-Williams L; de Montigny S
Can Commun Dis Rep; 2020 Jun; 46(6):192-197. PubMed ID: 32673377
[TBL] [Abstract][Full Text] [Related]
19. Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia.
Tan CV; Singh S; Lai CH; Zamri ASSM; Dass SC; Aris TB; Ibrahim HM; Gill BS
Int J Environ Res Public Health; 2022 Jan; 19(3):. PubMed ID: 35162523
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
