Financial time series forecasting using independent component analysis and support vector regression

By Chi-Jie Lu, Tian-Shyug Lee, Chih-Chou Chiu. 2009

547 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing

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Citations in the corpus

Incremental learning for ν-Support Vector Regression (2015, background): “where, following Schölkopf et al. (2000), training samples xi are mapped into a high dimensional reproducing kernel Hilbert space (RKHS) (Schölkopf & Smola, 2001) by the transformation function φ. K(xi, xj) = ⟨φ(xi), φ(xj)⟩, ⟨·, ·⟩ denotes inner product in RKHS. (∗) is a shorthand implying both the ”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, methodology): “Traditional analytic tools, such as support vector regression (SVR) [14], [15], gradient boosting decision tree (GBDT) [16], [17], vector au-”
Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting (2012, methodology): “However, it is widely known that financial time series forecasting has shortcomings, including its inherent nonlinearity and non-stationarity (Huang et al., 2010; Lu et al., 2009).”
Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms (2021, methodology): “Basically, the SVR model was developed using the algorithm of support vector machine (SVM) classifiers [74].”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “This can be attributed to the ability of NN to self-adapt to any nonlinear data set without any statically assumption and prior knowledge of the data set [152].”
A Feature Fusion Based Forecasting Model for Financial Time Series (2014, methodology): “Lu et al [20] proposed a method to predict time series using ICA as a pre-processing tool.”
Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation (2019, background): “[91] Lu, C.”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “[260] Financial time series forecasting Independent component analysis and SVR SVR model with nonfiltered forecasting variables and a random walk model Hybrid models performs better”
Portfolio Selection : A Survey (2014, unknown): “2004], and support vector machines [Tay and Cao 2002; Cao and Tay 2003; Lu et al. 2009], and boosting and expert weighting [Creamer 2007; Creamer and Freund 2007, 2010; Creamer 2012].”
Financial time series forecasting with machine learning techniques: a survey (2010, unknown): “Time-frame Number Publications Day 31 [1], [2], [3], [4], [6], [7], [8], [9], [10], [13], [14], [17], [19], [20], [21], [22], [24], [27], [28], [31], [32], [33], [34], [35], [36], [37], [40], [41], [42], [44], [45] Week 3 [18], [23], [43] Month 3 [26], [38], [39] Multiple / Other 9 [5], [11], [12], ”
Measure Transformed Independent Component Analysis (2013, background): “trained to be orthogonal. The mutual independence assumption is plausible in a wide variety of elds, including telecommunications (Qin et al., 2009; Zhao et al., 2009), nance (Back and Weigend, 1997; Liu et al., 2009), and biomedical signal analysis (Makeig et al., 1 arXiv:1302.0730v2 [stat.ME] 9 De”
Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects (2014, background): “Moreover, they present a remarkable ability of balancing model accuracy and model complexity, depending on the available data (see amongst others Montana and Parrella (2008) and Lu et al. (2009)).”