5,292 research outputs found
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Sequential support vector classifiers and regression
Support Vector Machines (SVMs) map the input training data into a high dimensional feature space and finds a maximal margin hyperplane separating the data in that feature space. Extensions of this approach account for non-separable or noisy training data (soft classifiers) as well as support vector based regression. The optimal hyperplane is usually found by solving a quadratic programming problem which is usually quite complex, time consuming and prone to numerical instabilities. In this work, we introduce a sequential gradient ascent based algorithm for fast and simple implementation of the SVM for classification with soft classifiers. The fundamental idea is similar to applying the Adatron algorithm to SVM as developed independently in the Kernel-Adatron [7], although the details are different in many respects. We modify the formulation of the bias and consider a modified dual optimization problem. This formulation has made it possible to extend the framework for solving the SVM regression in an online setting. This paper looks at theoretical justifications of the algorithm, which is shown to converge robustly to the optimal solution very fast in terms of number of iterations, is orders of magnitude faster than conventional SVM solutions and is extremely simple to implement even for large sized problems. Experimental evaluations on benchmark classification problems of sonar data and USPS and MNIST databases substantiate the speed and robustness of the learning procedure
Scalable Kernel Methods via Doubly Stochastic Gradients
The general perception is that kernel methods are not scalable, and neural
nets are the methods of choice for nonlinear learning problems. Or have we
simply not tried hard enough for kernel methods? Here we propose an approach
that scales up kernel methods using a novel concept called "doubly stochastic
functional gradients". Our approach relies on the fact that many kernel methods
can be expressed as convex optimization problems, and we solve the problems by
making two unbiased stochastic approximations to the functional gradient, one
using random training points and another using random functions associated with
the kernel, and then descending using this noisy functional gradient. We show
that a function produced by this procedure after iterations converges to
the optimal function in the reproducing kernel Hilbert space in rate ,
and achieves a generalization performance of . This doubly
stochasticity also allows us to avoid keeping the support vectors and to
implement the algorithm in a small memory footprint, which is linear in number
of iterations and independent of data dimension. Our approach can readily scale
kernel methods up to the regimes which are dominated by neural nets. We show
that our method can achieve competitive performance to neural nets in datasets
such as 8 million handwritten digits from MNIST, 2.3 million energy materials
from MolecularSpace, and 1 million photos from ImageNet.Comment: 32 pages, 22 figure
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