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 *

119 related articles for article (PubMed ID: 36584161)

  • 1. Forecasting and change point test for nonlinear heteroscedastic time series based on support vector regression.
    Wang H; Guo M; Lee S; Chua CH
    PLoS One; 2022; 17(12):e0278816. PubMed ID: 36584161
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression.
    Lee S; Kim CK; Lee S
    Entropy (Basel); 2020 May; 22(5):. PubMed ID: 33286350
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Robust control chart for nonlinear conditionally heteroscedastic time series based on Huber support vector regression.
    Kim CK; Yoon MH; Lee S
    PLoS One; 2024; 19(2):e0299120. PubMed ID: 38394080
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.
    Bildirici M; Ersin Ö
    ScientificWorldJournal; 2014; 2014():497941. PubMed ID: 24977200
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Monitoring Volatility Change for Time Series Based on Support Vector Regression.
    Lee S; Kim CK; Kim D
    Entropy (Basel); 2020 Nov; 22(11):. PubMed ID: 33287077
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Modeling variations in the cedi/dollar exchange rate in Ghana: an autoregressive conditional heteroscedastic (ARCH) models.
    Techie Quaicoe M; Twenefour FB; Baah EM; Nortey EN
    Springerplus; 2015; 4():329. PubMed ID: 26180749
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Change Point Test for the Conditional Mean of Time Series of Counts Based on Support Vector Regression.
    Lee S; Lee S
    Entropy (Basel); 2021 Apr; 23(4):. PubMed ID: 33917192
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework.
    Ou J; Huang X; Zhou Y; Zhou Z; Nie Q
    Entropy (Basel); 2022 Sep; 24(10):. PubMed ID: 37420412
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Modelling and forecasting of growth rate of new COVID-19 cases in top nine affected countries: Considering conditional variance and asymmetric effect.
    Ekinci A
    Chaos Solitons Fractals; 2021 Oct; 151():111227. PubMed ID: 34253942
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Estimation of the parameters of symmetric stable ARMA and ARMA-GARCH models.
    Sathe AM; Upadhye NS
    J Appl Stat; 2022; 49(11):2964-2980. PubMed ID: 35909668
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Detecting Structural Change Point in ARMA Models via Neural Network Regression and LSCUSUM Methods.
    Ri XH; Chen Z; Liang Y
    Entropy (Basel); 2023 Jan; 25(1):. PubMed ID: 36673274
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Forecasting riverine total nitrogen loads using wavelet analysis and support vector regression combination model in an agricultural watershed.
    Ji X; Lu J
    Environ Sci Pollut Res Int; 2018 Sep; 25(26):26405-26422. PubMed ID: 29982944
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Electricity Consumption Forecasting using Support Vector Regression with the Mixture Maximum Correntropy Criterion.
    Duan J; Tian X; Ma W; Qiu X; Wang P; An L
    Entropy (Basel); 2019 Jul; 21(7):. PubMed ID: 33267421
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.
    Zhang H; Zhang S; Wang P; Qin Y; Wang H
    J Air Waste Manag Assoc; 2017 Jul; 67(7):776-788. PubMed ID: 28278031
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Novel Forecasting Approach by the GA-SVR-GRNN Hybrid Deep Learning Algorithm for Oil Future Prices.
    Wang L; Xia Y; Lu Y
    Comput Intell Neurosci; 2022; 2022():4952215. PubMed ID: 36045986
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Monitoring parameter change for bivariate time series models of counts.
    Lee S; Kim D
    J Korean Stat Soc; 2023 May; ():1-23. PubMed ID: 37361425
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Daily air quality index forecasting with hybrid models: A case in China.
    Zhu S; Lian X; Liu H; Hu J; Wang Y; Che J
    Environ Pollut; 2017 Dec; 231(Pt 2):1232-1244. PubMed ID: 28939124
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Noise model based ν-support vector regression with its application to short-term wind speed forecasting.
    Hu Q; Zhang S; Xie Z; Mi J; Wan J
    Neural Netw; 2014 Sep; 57():1-11. PubMed ID: 24874183
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Hybrid System Based on Dynamic Selection for Time Series Forecasting.
    de Oliveira JFL; Silva EG; de Mattos Neto PSG
    IEEE Trans Neural Netw Learn Syst; 2022 Aug; 33(8):3251-3263. PubMed ID: 33513115
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.
    Alwee R; Shamsuddin SM; Sallehuddin R
    ScientificWorldJournal; 2013; 2013():951475. PubMed ID: 23766729
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.