A hybrid ARIMA and support vector machines model in stock price forecasting

By Ping-Feng Pai, Chih-Sheng Lin. 2005

1,013 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “Popular machine learning methods in HFT include regression analysis [1], [2], [3], [4], [5], multilayer feed forward network [6], [7], [8], convolutional neural network [9] , recurrent neural network [10], [11], [12].”
A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting (2014, result): “…model took successful results in the short time in many areas for instance rainfall forecasting (Hong & Pai, 2007), reliability forecasting (Pai & Hong, 2006), electric load (Hong, 2009), wind speed forecasting (Mohandes, Halawani, Rehman, & Hussain, 2004) and financial problems (Pai & Lin, 2005).”
Textual analysis of stock market prediction using breaking financial news: The AZFin text system (2009, methodology): “In order to gain insight into the performance of our results, we can compare them to Pai and Lin whom conducted a similar study on forecasting stock prices [20].”
An artificial neural network (p, d, q) model for timeseries forecasting (2010, background): “Pai and Lin (2005) proposed a hybrid methodology to exploit the unique strength of ARIMA models and support vector machines (SVMs) for stock prices forecasting.”
Support vector regression with chaos-based firefly algorithm for stock market price forecasting (2013, background): “…of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia c Islamic Azad University, Tafresh Branch, Young Researchers Club, Tafresh, Iran d School of Information Systems, Curtin University, Perth, WA, Australia a r t i c l e i n f o”
Surveying stock market forecasting techniques - Part II: Soft computing methods (2009, background): “9), Pantazopoulos et al. (1998), Rech (2002), Thawornwong and Enke (2004), Tsaih , Kanas and Yannopoulos (2001) and Rech (2002)”
Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression (2017, background): “3 Hybriding LSSVR and PSO for carbon price forecasting 22 The model selection of LSSVR is concerned with two key issues [29,30]: how to select an appropriate kernel 23 function, and how to determine the optimal parameters of LSSVR.”
Predicting the Price of Bitcoin Using Machine Learning (2018, neutral): “[38], [39]).”
A multiple-kernel support vector regression approach for stock market price forecasting (2011, neutral): “…(FNN) (Chang & Liu, 2008; Oh, Pedrycz, & Park, 2006; Zarandi, Rezaee, Turksen, & Neshat, 2009), and support vector regression (SVR) (Cao & Tay, 2001, 2003; Fernando, Julio, & Javier, 2003; Gestel et al., 2001; Pai & Lin, 2005; Tay & Cao, 2001; Valeriy & Supriya, 2006; Yang, Chan, & King, 2002).”
ARIMA Model in Predicting Banking Stock Market Data (2018, background): “Banking time series data and financial time series in general is very hard in decomposition and forecasting because the data are non-stationary and non-linear with high heteroscedasticity (Pai & Lin, 2005; Wang et al., 2012; Wei, 2013).”