21,034 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
Localized Lasso for High-Dimensional Regression
We introduce the localized Lasso, which is suited for learning models that
are both interpretable and have a high predictive power in problems with high
dimensionality and small sample size . More specifically, we consider a
function defined by local sparse models, one at each data point. We introduce
sample-wise network regularization to borrow strength across the models, and
sample-wise exclusive group sparsity (a.k.a., norm) to introduce
diversity into the choice of feature sets in the local models. The local models
are interpretable in terms of similarity of their sparsity patterns. The cost
function is convex, and thus has a globally optimal solution. Moreover, we
propose a simple yet efficient iterative least-squares based optimization
procedure for the localized Lasso, which does not need a tuning parameter, and
is guaranteed to converge to a globally optimal solution. The solution is
empirically shown to outperform alternatives for both simulated and genomic
personalized medicine data
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