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https://www.sciencedirect.com/science/article/pii/S0957417409010185
Support vector regression The seasonal time series is a sequence of seasonal data points recorded sequentially in time. Over the past several decades, many works have been devoted to develop and improve seasonal time series forecasting models.Cited by: 70
https://www.researchgate.net/publication/220219323_Time_series_forecasting_by_a_seasonal_support_vector_regression_model
The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of SVR models in a seasonal time...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380825/
Feb 08, 2019 · The incidence data of HFMD of Wuhan city from January 2009 to December 2016 were used to fit a combined model with seasonal autoregressive integrated moving average (SARIMA) model and support vector regression (SVR) model. Then, the SARIMA-SVR hybrid model was constructed.Cited by: 1
https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2014-94.pdf
Iterative ARIMA-Multiple Support Vector Regression models for long term time series prediction Jo˜ao Fausto Lorenzato de Oliveira and Teresa B. Ludermir Federal University of Pernambuco - Center of informatics Av. Jornalista Anibal Fernandes, s/n, Recife, PE, 50.740-560, Brazil Abstract. Support Vector Regression (SVR) has been widely applied in
http://www.realtechsupport.org/UB/SR/time/Agrawal_TimeSeriesAnalysis.pdf
An Introductory Study on Time Series Modeling and Forecasting Ratnadip Adhikari R. K. Agrawal ... models. For seasonal time series forecasting, Box and Jenkins [6] had proposed a quite successful variation of ARIMA model, viz. the Seasonal ... A major breakthrough in the area of time series forecasting occurred with the . support vector SVM ...
https://towardsdatascience.com/3-facts-about-time-series-forecasting-that-surprise-experienced-machine-learning-practitioners-69c18ee89387
Sep 12, 2018 · At the crux of this disconnect is that time series forecasting can be cast as a supervised learning problem, and hence the entire arsenal of ML methods — Regression, Neural Networks, Support Vector Machines, Random Forests, XGBoost, etc…. — can be thrown at it.
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