Support vector machine with adaptive parameters in financial time series forecasting

By Lijuan Cao, Francis Eng Hock Tay. IEEE Trans. Neural Networks, 2003

1,018 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/a244c47a1d4a8c2894b22807df8c7eec16cc110a

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Citations in the corpus

Prediction-based portfolio optimization model using neural networks (2009, background): “However, in the last two decades, many machine learning methods for time series prediction appeared, ranging from neural network models to support vector machines and fuzzy sets [13] [14] [15] [16].”
A multiple-kernel support vector regression approach for stock market price forecasting (2011, neutral): “…neural networks (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, &…”
Incremental learning for ν-Support Vector Regression (2015, neutral): “In real-world regression tasks, such as time-series prediction (e.g. Cao and Tay (2003); Lu, Lee, and Chiu (2009)), training data is usually provided sequentially, in the extreme case, one example at a time, which is an online scenario (Murata, 1998).”
Time Series Prediction Using Support Vector Machines: A Survey (2009, background): “They published several other related SVR applications for financial data time series prediction [31] [32] [33] [39].”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “Traditional analytic tools, such as supportvectorregression(SVR)[19],gradientboostingdecision tree (GBDT) [20], vector autoregressive (VAR) [21], and autoregressive integrated moving average (ARIMA) [22], struggle to handle complex time series relations (e.g., nonlinearities and inter-variable relat”
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”
A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting (2014, methodology): “The SVR model showed excellent performance in various prediction fields including financial applications (Cao & Tay, 2003; Dianmin, Gao, & Guan, 2004; Sadat Mostafavi, Mostafavi, Jaafari, & Hosseinpour, 2013; Shin, Taik, & Hyun-jung, 2005; Valeriy & Supriya, 2006) and atmospheric science prediction…”
Parameter determination of support vector machine and feature selection using simulated annealing approach (2008, methodology): “SVM is an emerging data classification technique first developed by Vapnik [1], and has been widely adopted in various fields of classification problems recently [2] [3] [4] [5] [6] [7] [8] [9].”
An Introductory Study on Time Series Modeling and Forecasting (2013, background): “Furthermore a continuous time series can be easily transformed to a discrete one by merging data together over a specified time interval.”
Financial time series prediction using polynomial pipelined neural networks (2008, methodology): “This modification to the data distribution may improve the predictive power of the neural networks (Cao & Tay, 2003).”
Temporal pattern attention for multivariate time series forecasting (2018, background): “Additionally, linear support vector regression (SVR) [Cao and Tay(2003), Kim(2003)] treats the forecasting problem as a typical regression problem with time-varying parameters.”
Financial time series forecasting using independent component analysis and support vector regression (2009, methodology): “It has been successfully applied in different problems of time series prediction such as production value forecast of machinery industry, engine reliability prediction, wind speed prediction and financial time series forecasting [7, 9, 26, 30, 45, 48, 55, 56, 57].”
Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014, methodology): “Tay et al. [42] [43] and Cao et al. [3] modify the SVM objective function and make C an adaptive parameter for nonstationary time series.”
A Hybrid Neurogenetic Approach for Stock Forecasting (2007, methodology): “Accuracies. performance of time-series data [4], [39].”