These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

146 related articles for article (PubMed ID: 38331569)

  • 21. Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.
    Zhang X; Pang Y; Cui M; Stallones L; Xiang H
    Ann Epidemiol; 2015 Feb; 25(2):101-6. PubMed ID: 25467006
    [TBL] [Abstract][Full Text] [Related]  

  • 22. 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]  

  • 23. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.
    Mao Q; Zhang K; Yan W; Cheng C
    J Infect Public Health; 2018; 11(5):707-712. PubMed ID: 29730253
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Forecasting of monthly relative humidity in Delhi, India, using SARIMA and ANN models.
    Shad M; Sharma YD; Singh A
    Model Earth Syst Environ; 2022; 8(4):4843-4851. PubMed ID: 35434264
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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]  

  • 26. Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.
    Chen Q; Zheng X; Shi H; Zhou Q; Hu H; Sun M; Xu Y; Zhang X
    BMC Public Health; 2024 May; 24(1):1399. PubMed ID: 38796443
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries.
    Choi SB; Kim J; Ahn I
    PLoS One; 2019; 14(11):e0220423. PubMed ID: 31765386
    [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. 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]  

  • 30. Forecasting blood demand for different blood groups in Shiraz using auto regressive integrated moving average (ARIMA) and artificial neural network (ANN) and a hybrid approaches.
    Sarvestani SE; Hatam N; Seif M; Kasraian L; Lari FS; Bayati M
    Sci Rep; 2022 Dec; 12(1):22031. PubMed ID: 36539511
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. Prediction of reported monthly incidence of hepatitis B in Hainan Province of China based on SARIMA-BPNN model.
    Fang K; Cao L; Fu Z; Li W
    Medicine (Baltimore); 2023 Oct; 102(41):e35054. PubMed ID: 37832091
    [TBL] [Abstract][Full Text] [Related]  

  • 33. 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]  

  • 34. Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model.
    Wang Y; Xu C; Wang Z; Zhang S; Zhu Y; Yuan J
    PLoS One; 2018; 13(12):e0208404. PubMed ID: 30586416
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018.
    Tian CW; Wang H; Luo XM
    Epidemiol Infect; 2019 Jan; 147():e82. PubMed ID: 30868999
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models.
    Chen Q; Zhao H; Qiu H; Wang Q; Zeng D; Ye M
    J Infect Dev Ctries; 2022 Aug; 16(8):1343-1350. PubMed ID: 36099379
    [TBL] [Abstract][Full Text] [Related]  

  • 37. 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]  

  • 38. Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease.
    Xian X; Wang L; Wu X; Tang X; Zhai X; Yu R; Qu L; Ye M
    BMC Infect Dis; 2023 Nov; 23(1):803. PubMed ID: 37974072
    [TBL] [Abstract][Full Text] [Related]  

  • 39. The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China.
    Feng T; Zheng Z; Xu J; Liu M; Li M; Jia H; Yu X
    Front Public Health; 2022; 10():946563. PubMed ID: 35937210
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Comparative study of four time series methods in forecasting typhoid fever incidence in China.
    Zhang X; Liu Y; Yang M; Zhang T; Young AA; Li X
    PLoS One; 2013; 8(5):e63116. PubMed ID: 23650546
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 8.