327 research outputs found
Financial time series forecasting using twin support vector regression
© 2019 Gupta et al. Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function
Support vector regression (SVR) has garnered significant popularity over the
past two decades owing to its wide range of applications across various fields.
Despite its versatility, SVR encounters challenges when confronted with
outliers and noise, primarily due to the use of the -insensitive
loss function. To address this limitation, SVR with bounded loss functions has
emerged as an appealing alternative, offering enhanced generalization
performance and robustness. Notably, recent developments focus on designing
bounded loss functions with smooth characteristics, facilitating the adoption
of gradient-based optimization algorithms. However, it's crucial to highlight
that these bounded and smooth loss functions do not possess an insensitive
zone. In this paper, we address the aforementioned constraints by introducing a
novel symmetric loss function named the HawkEye loss function. It is worth
noting that the HawkEye loss function stands out as the first loss function in
SVR literature to be bounded, smooth, and simultaneously possess an insensitive
zone. Leveraging this breakthrough, we integrate the HawkEye loss function into
the least squares framework of SVR and yield a new fast and robust model termed
HE-LSSVR. The optimization problem inherent to HE-LSSVR is addressed by
harnessing the adaptive moment estimation (Adam) algorithm, known for its
adaptive learning rate and efficacy in handling large-scale problems. To our
knowledge, this is the first time Adam has been employed to solve an SVR
problem. To empirically validate the proposed HE-LSSVR model, we evaluate it on
UCI, synthetic, and time series datasets. The experimental outcomes
unequivocally reveal the superiority of the HE-LSSVR model both in terms of its
remarkable generalization performance and its efficiency in training time
Solution Path Algorithm for Twin Multi-class Support Vector Machine
The twin support vector machine and its extensions have made great
achievements in dealing with binary classification problems, however, which is
faced with some difficulties such as model selection and solving
multi-classification problems quickly. This paper is devoted to the fast
regularization parameter tuning algorithm for the twin multi-class support
vector machine. A new sample dataset division method is adopted and the
Lagrangian multipliers are proved to be piecewise linear with respect to the
regularization parameters by combining the linear equations and block matrix
theory. Eight kinds of events are defined to seek for the starting event and
then the solution path algorithm is designed, which greatly reduces the
computational cost. In addition, only few points are combined to complete the
initialization and Lagrangian multipliers are proved to be 1 as the
regularization parameter tends to infinity. Simulation results based on UCI
datasets show that the proposed method can achieve good classification
performance with reducing the computational cost of grid search method from
exponential level to the constant level
Study on support vector machine as a classifier
SVM [1], [2] is a learning method which learns by considering data points to be in space. We
studied different types of Support Vector Machine (SVM). We also observed their
classification process. We conducted10-fold testing experiments on LSSVM [7], [8] (Least
square Support Vector Machine) and PSVM [9] (Proximal Support Vector Machine) using
standard sets of data. Finally we proposed a new algorithm NPSVM (Non-Parallel Support
Vector Machine) which is reformulated from NPPC [12], [13] (Non-Parallel Plane
Classifier). We have observed that the cost function of NPPC is affected by the additional
constraint for Euclidean distance classification. So we implicitly normalized the weight
vectors instead of the additional constraint. As a result we could generate a very good cost
function. The computational complexity of NPSVM for both linear and non-linear kernel is
evaluated. The results of 10-fold test using standard data sets of NPSVM are compared with
the LSSVM and PSVM
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