151 related articles for article (PubMed ID: 38076174)
1. Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints.
Agyemang EF; Mensah JA; Ocran E; Opoku E; Nortey ENN
Heliyon; 2023 Dec; 9(12):e22544. PubMed ID: 38076174
[TBL] [Abstract][Full Text] [Related]
2. Time trends in gender-specific incidence rates of road traffic injuries in Iran.
Delavary Foroutaghe M; Mohammadzadeh Moghaddam A; Fakoor V
PLoS One; 2019; 14(5):e0216462. PubMed ID: 31071156
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. A Time Series Model for Assessing the Trend and Forecasting the Road Traffic Accident Mortality.
Yousefzadeh-Chabok S; Ranjbar-Taklimie F; Malekpouri R; Razzaghi A
Arch Trauma Res; 2016 Sep; 5(3):e36570. PubMed ID: 27800467
[TBL] [Abstract][Full Text] [Related]
6. A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China.
Luo Z; Jia X; Bao J; Song Z; Zhu H; Liu M; Yang Y; Shi X
Int J Environ Res Public Health; 2022 May; 19(10):. PubMed ID: 35627447
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Forecasting time trends of fatal motor vehicle crashes in Iran using an ensemble learning algorithm.
Nassiri H; Mohammadpour SI; Dahaghin M
Traffic Inj Prev; 2023; 24(1):44-49. PubMed ID: 36278888
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Forecasting time trend of road traffic crashes in Iran using the macro-scale traffic flow characteristics.
Nassiri H; Mohammadpour SI; Dahaghin M
Heliyon; 2023 Mar; 9(3):e14481. PubMed ID: 36967875
[TBL] [Abstract][Full Text] [Related]
11. Application of Time-Series Analysis and Expert Judgment in Modeling and Forecasting Blood Donation Trends in Zimbabwe.
Chideme C; Chikobvu D
MDM Policy Pract; 2024; 9(1):23814683231222483. PubMed ID: 38250667
[TBL] [Abstract][Full Text] [Related]
12. Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.
Parvareh M; Karimi A; Rezaei S; Woldemichael A; Nili S; Nouri B; Nasab NE
Burns Trauma; 2018; 6():9. PubMed ID: 29556507
[TBL] [Abstract][Full Text] [Related]
13. Forecasting incidence of dengue in Rajasthan, using time series analyses.
Bhatnagar S; Lal V; Gupta SD; Gupta OP
Indian J Public Health; 2012; 56(4):281-5. PubMed ID: 23354138
[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. Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil.
Pimentel KBA; Oliveira RS; Aragão CF; Aquino Júnior J; Moura MES; Guimarães-E-Silva AS; Pinheiro VCS; Gonçalves EGR; Silva AR
Braz J Biol; 2022; 84():e257402. PubMed ID: 35081217
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. SARIMA Model Forecasting Performance of the COVID-19 Daily Statistics in Thailand during the Omicron Variant Epidemic.
Duangchaemkarn K; Boonchieng W; Wiwatanadate P; Chouvatut V
Healthcare (Basel); 2022 Jul; 10(7):. PubMed ID: 35885836
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models.
Wang Y; Yan Z; Wang D; Yang M; Li Z; Gong X; Wu D; Zhai L; Zhang W; Wang Y
BMC Infect Dis; 2022 May; 22(1):495. PubMed ID: 35614387
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
